1.0.0 F5-TTS v1 base model with better training and inference performance

.github/workflows/publish-pypi.yaml

@@ -0,0 +1,66 @@
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+# GitHub recommends pinning actions to a commit SHA.
+# To get a newer version, you will need to update the SHA.
+# You can also reference a tag or branch, but the action may change without warning.
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  release-build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.x"
+
+      - name: Build release distributions
+        run: |
+          # NOTE: put your own distribution build steps here.
+          python -m pip install build
+          python -m build
+
+      - name: Upload distributions
+        uses: actions/upload-artifact@v4
+        with:
+          name: release-dists
+          path: dist/
+
+  pypi-publish:
+    runs-on: ubuntu-latest
+
+    needs:
+      - release-build
+
+    permissions:
+      # IMPORTANT: this permission is mandatory for trusted publishing
+      id-token: write
+
+    # Dedicated environments with protections for publishing are strongly recommended.
+    environment:
+      name: pypi
+      # OPTIONAL: uncomment and update to include your PyPI project URL in the deployment status:
+      # url: https://pypi.org/p/YOURPROJECT
+
+    steps:
+      - name: Retrieve release distributions
+        uses: actions/download-artifact@v4
+        with:
+          name: release-dists
+          path: dist/
+
+      - name: Publish release distributions to PyPI
+        uses: pypa/gh-action-pypi-publish@6f7e8d9c0b1a2c3d4e5f6a7b8c9d0e1f2a3b4c5d

README.md

@@ -18,6 +18,7 @@
 ### Thanks to all the contributors !
 
 ## News
+- **2025/03/12**: F5-TTS v1 base model with better training and inference performance.
 - **2024/10/08**: F5-TTS & E2 TTS base models on [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS), [🤖 Model Scope](https://www.modelscope.cn/models/SWivid/F5-TTS_Emilia-ZH-EN), [🟣 Wisemodel](https://wisemodel.cn/models/SJTU_X-LANCE/F5-TTS_Emilia-ZH-EN).
 
 ## Installation
@@ -37,7 +38,7 @@ conda activate f5-tts
 
 > ```bash
 > # Install pytorch with your CUDA version, e.g.
-> pip install torch==2.3.0+cu118 torchaudio==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
+> pip install torch==2.4.0+cu124 torchaudio==2.4.0+cu124 --extra-index-url https://download.pytorch.org/whl/cu124
 > ```
 
 </details>
@@ -159,7 +160,7 @@ volumes:
 # Run with flags
 # Leave --ref_text "" will have ASR model transcribe (extra GPU memory usage)
 f5-tts_infer-cli \
---model "F5-TTS" \
+--model "F5-TTS_v1" \
 --ref_audio "ref_audio.wav" \
 --ref_text "The content, subtitle or transcription of reference audio." \
 --gen_text "Some text you want TTS model generate for you."

ckpts/README.md

@@ -3,8 +3,10 @@ Pretrained model ckpts. https://huggingface.co/SWivid/F5-TTS
 
 ```
 ckpts/
-    E2TTS_Base/
-        model_1200000.pt
+    F5TTS_v1_Base/
+        model_1250000.safetensors
     F5TTS_Base/
-        model_1200000.pt
+        model_1200000.safetensors
+    E2TTS_Base/
+        model_1200000.safetensors
 ```

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "f5-tts"
-version = "0.6.2"
+version = "1.0.0"
 description = "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"
 readme = "README.md"
 license = {text = "MIT License"}
@@ -25,7 +25,6 @@ dependencies = [
     "jieba",
     "librosa",
     "matplotlib",
-    "nltk",
     "numpy<=1.26.4",
     "pydub",
     "pypinyin",

src/f5_tts/api.py

@@ -5,43 +5,43 @@ from importlib.resources import files
 import soundfile as sf
 import tqdm
 from cached_path import cached_path
+from omegaconf import OmegaConf
 
 from f5_tts.infer.utils_infer import (
-    hop_length,
-    infer_process,
     load_model,
     load_vocoder,
+    transcribe,
     preprocess_ref_audio_text,
+    infer_process,
     remove_silence_for_generated_wav,
     save_spectrogram,
-    transcribe,
-    target_sample_rate,
 )
-from f5_tts.model import DiT, UNetT
+from f5_tts.model import DiT, UNetT  # noqa: F401. used for config
 from f5_tts.model.utils import seed_everything
 
 
 class F5TTS:
     def __init__(
         self,
-        model_type="F5-TTS",
+        model="F5TTS_v1_Base",
         ckpt_file="",
         vocab_file="",
         ode_method="euler",
         use_ema=True,
-        vocoder_name="vocos",
-        local_path=None,
+        vocoder_local_path=None,
         device=None,
         hf_cache_dir=None,
     ):
-        # Initialize parameters
-        self.final_wave = None
-        self.target_sample_rate = target_sample_rate
-        self.hop_length = hop_length
-        self.seed = -1
-        self.mel_spec_type = vocoder_name
-
-        # Set device
+        model_cfg = OmegaConf.load(str(files("f5_tts").joinpath(f"configs/{model}.yaml")))
+        model_cls = globals()[model_cfg.model.backbone]
+        model_arc = model_cfg.model.arch
+
+        self.mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+        self.target_sample_rate = model_cfg.model.mel_spec.target_sample_rate
+
+        self.ode_method = ode_method
+        self.use_ema = use_ema
+
         if device is not None:
             self.device = device
         else:
@@ -58,39 +58,31 @@ class F5TTS:
             )
 
         # Load models
-        self.load_vocoder_model(vocoder_name, local_path=local_path, hf_cache_dir=hf_cache_dir)
-        self.load_ema_model(
-            model_type, ckpt_file, vocoder_name, vocab_file, ode_method, use_ema, hf_cache_dir=hf_cache_dir
+        self.vocoder = load_vocoder(
+            self.mel_spec_type, vocoder_local_path is not None, vocoder_local_path, self.device, hf_cache_dir
         )
 
-    def load_vocoder_model(self, vocoder_name, local_path=None, hf_cache_dir=None):
-        self.vocoder = load_vocoder(vocoder_name, local_path is not None, local_path, self.device, hf_cache_dir)
-
-    def load_ema_model(self, model_type, ckpt_file, mel_spec_type, vocab_file, ode_method, use_ema, hf_cache_dir=None):
-        if model_type == "F5-TTS":
-            if not ckpt_file:
-                if mel_spec_type == "vocos":
-                    ckpt_file = str(
-                        cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors", cache_dir=hf_cache_dir)
-                    )
-                elif mel_spec_type == "bigvgan":
-                    ckpt_file = str(
-                        cached_path("hf://SWivid/F5-TTS/F5TTS_Base_bigvgan/model_1250000.pt", cache_dir=hf_cache_dir)
-                    )
-            model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
-            model_cls = DiT
-        elif model_type == "E2-TTS":
-            if not ckpt_file:
-                ckpt_file = str(
-                    cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors", cache_dir=hf_cache_dir)
-                )
-            model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
-            model_cls = UNetT
+        repo_name, ckpt_step, ckpt_type = "F5-TTS", 1250000, "safetensors"
+
+        # override for previous models
+        if model == "F5TTS_Base":
+            if self.mel_spec_type == "vocos":
+                ckpt_step = 1200000
+            elif self.mel_spec_type == "bigvgan":
+                model = "F5TTS_Base_bigvgan"
+                ckpt_type = "pt"
+        elif model == "E2TTS_Base":
+            repo_name = "E2-TTS"
+            ckpt_step = 1200000
         else:
-            raise ValueError(f"Unknown model type: {model_type}")
+            raise ValueError(f"Unknown model type: {model}")
 
+        if not ckpt_file:
+            ckpt_file = str(
+                cached_path(f"hf://SWivid/{repo_name}/{model}/model_{ckpt_step}.{ckpt_type}", cache_dir=hf_cache_dir)
+            )
         self.ema_model = load_model(
-            model_cls, model_cfg, ckpt_file, mel_spec_type, vocab_file, ode_method, use_ema, self.device
+            model_cls, model_arc, ckpt_file, self.mel_spec_type, vocab_file, self.ode_method, self.use_ema, self.device
         )
 
     def transcribe(self, ref_audio, language=None):
@@ -102,8 +94,8 @@ class F5TTS:
         if remove_silence:
             remove_silence_for_generated_wav(file_wave)
 
-    def export_spectrogram(self, spect, file_spect):
-        save_spectrogram(spect, file_spect)
+    def export_spectrogram(self, spec, file_spec):
+        save_spectrogram(spec, file_spec)
 
     def infer(
         self,
@@ -121,17 +113,16 @@ class F5TTS:
         fix_duration=None,
         remove_silence=False,
         file_wave=None,
-        file_spect=None,
-        seed=-1,
+        file_spec=None,
+        seed=None,
     ):
-        if seed == -1:
-            seed = random.randint(0, sys.maxsize)
-        seed_everything(seed)
-        self.seed = seed
+        if seed is None:
+            self.seed = random.randint(0, sys.maxsize)
+        seed_everything(self.seed)
 
         ref_file, ref_text = preprocess_ref_audio_text(ref_file, ref_text, device=self.device)
 
-        wav, sr, spect = infer_process(
+        wav, sr, spec = infer_process(
             ref_file,
             ref_text,
             gen_text,
@@ -153,22 +144,22 @@ class F5TTS:
         if file_wave is not None:
             self.export_wav(wav, file_wave, remove_silence)
 
-        if file_spect is not None:
-            self.export_spectrogram(spect, file_spect)
+        if file_spec is not None:
+            self.export_spectrogram(spec, file_spec)
 
-        return wav, sr, spect
+        return wav, sr, spec
 
 
 if __name__ == "__main__":
     f5tts = F5TTS()
 
-    wav, sr, spect = f5tts.infer(
+    wav, sr, spec = f5tts.infer(
         ref_file=str(files("f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav")),
         ref_text="some call me nature, others call me mother nature.",
         gen_text="""I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences.""",
         file_wave=str(files("f5_tts").joinpath("../../tests/api_out.wav")),
-        file_spect=str(files("f5_tts").joinpath("../../tests/api_out.png")),
-        seed=-1,  # random seed = -1
+        file_spec=str(files("f5_tts").joinpath("../../tests/api_out.png")),
+        seed=None,
     )
 
     print("seed :", f5tts.seed)

src/f5_tts/configs/{E2TTS_Base_train.yaml → E2TTS_Base.yaml}

@@ -1,16 +1,16 @@
 hydra:
   run:
     dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
-    
+
 datasets:
   name: Emilia_ZH_EN  # dataset name
   batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
-  batch_size_type: frame  # "frame" or "sample"
+  batch_size_type: frame  # frame | sample
   max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
   num_workers: 16
 
 optim:
-  epochs: 15
+  epochs: 11
   learning_rate: 7.5e-5
   num_warmup_updates: 20000  # warmup updates
   grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
@@ -20,25 +20,29 @@ optim:
 model:
   name: E2TTS_Base
   tokenizer: pinyin
-  tokenizer_path: None  # if tokenizer = 'custom', define the path to the tokenizer you want to use (should be vocab.txt)
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: UNetT
   arch:
     dim: 1024
     depth: 24
     heads: 16
     ff_mult: 4
+    text_mask_padding: False
+    pe_attn_head: 1
   mel_spec:
     target_sample_rate: 24000
     n_mel_channels: 100
     hop_length: 256
     win_length: 1024
     n_fft: 1024
-    mel_spec_type: vocos  # 'vocos' or 'bigvgan'
+    mel_spec_type: vocos  # vocos | bigvgan
   vocoder:
     is_local: False  # use local offline ckpt or not
-    local_path: None  # local vocoder path
+    local_path: null  # local vocoder path
 
 ckpts:
-  logger: wandb  # wandb | tensorboard | None
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
   save_per_updates: 50000  # save checkpoint per updates
   keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
   last_per_updates: 5000  # save last checkpoint per updates

src/f5_tts/configs/{E2TTS_Small_train.yaml → E2TTS_Small.yaml}

@@ -1,16 +1,16 @@
 hydra:
   run:
     dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
-    
+
 datasets:
   name: Emilia_ZH_EN
   batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
-  batch_size_type: frame  # "frame" or "sample"
+  batch_size_type: frame  # frame | sample
   max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
   num_workers: 16
 
 optim:
-  epochs: 15
+  epochs: 11
   learning_rate: 7.5e-5
   num_warmup_updates: 20000  # warmup updates
   grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
@@ -20,25 +20,29 @@ optim:
 model:
   name: E2TTS_Small
   tokenizer: pinyin
-  tokenizer_path: None  # if tokenizer = 'custom', define the path to the tokenizer you want to use (should be vocab.txt)
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: UNetT
   arch:
     dim: 768
     depth: 20
     heads: 12
     ff_mult: 4
+    text_mask_padding: False
+    pe_attn_head: 1
   mel_spec:
     target_sample_rate: 24000
     n_mel_channels: 100
     hop_length: 256
     win_length: 1024
     n_fft: 1024
-    mel_spec_type: vocos  # 'vocos' or 'bigvgan'
+    mel_spec_type: vocos  # vocos | bigvgan
   vocoder:
     is_local: False  # use local offline ckpt or not
-    local_path: None  # local vocoder path
+    local_path: null  # local vocoder path
 
 ckpts:
-  logger: wandb  # wandb | tensorboard | None
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
   save_per_updates: 50000  # save checkpoint per updates
   keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
   last_per_updates: 5000  # save last checkpoint per updates

src/f5_tts/configs/{F5TTS_Base_train.yaml → F5TTS_Base.yaml}

@@ -1,16 +1,16 @@
 hydra:
   run:
     dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
-    
+
 datasets:
   name: Emilia_ZH_EN  # dataset name
   batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
-  batch_size_type: frame  # "frame" or "sample"
+  batch_size_type: frame  # frame | sample
   max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
   num_workers: 16
 
 optim:
-  epochs: 15
+  epochs: 11
   learning_rate: 7.5e-5
   num_warmup_updates: 20000  # warmup updates
   grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
@@ -20,14 +20,17 @@ optim:
 model:
   name: F5TTS_Base  # model name
   tokenizer: pinyin  # tokenizer type
-  tokenizer_path: None  # if tokenizer = 'custom', define the path to the tokenizer you want to use (should be vocab.txt)
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: DiT
   arch:
     dim: 1024
     depth: 22
     heads: 16
     ff_mult: 2
     text_dim: 512
+    text_mask_padding: False
     conv_layers: 4
+    pe_attn_head: 1
     checkpoint_activations: False  # recompute activations and save memory for extra compute
   mel_spec:
     target_sample_rate: 24000
@@ -35,13 +38,14 @@ model:
     hop_length: 256
     win_length: 1024
     n_fft: 1024
-    mel_spec_type: vocos  # 'vocos' or 'bigvgan'
+    mel_spec_type: vocos  # vocos | bigvgan
   vocoder:
     is_local: False  # use local offline ckpt or not
-    local_path: None  # local vocoder path
+    local_path: null  # local vocoder path
 
 ckpts:
-  logger: wandb  # wandb | tensorboard | None
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
   save_per_updates: 50000  # save checkpoint per updates
   keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
   last_per_updates: 5000  # save last checkpoint per updates

src/f5_tts/configs/{F5TTS_Small_train.yaml → F5TTS_Small.yaml}

@@ -1,16 +1,16 @@
 hydra:
   run:
     dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
-    
+
 datasets:
   name: Emilia_ZH_EN
   batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
-  batch_size_type: frame  # "frame" or "sample"
+  batch_size_type: frame  # frame | sample
   max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
   num_workers: 16
 
 optim:
-  epochs: 15
+  epochs: 11
   learning_rate: 7.5e-5
   num_warmup_updates: 20000  # warmup updates
   grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
@@ -20,14 +20,17 @@ optim:
 model:
   name: F5TTS_Small
   tokenizer: pinyin
-  tokenizer_path: None  # if tokenizer = 'custom', define the path to the tokenizer you want to use (should be vocab.txt)
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: DiT
   arch:
     dim: 768
     depth: 18
     heads: 12
     ff_mult: 2
     text_dim: 512
+    text_mask_padding: False
     conv_layers: 4
+    pe_attn_head: 1
     checkpoint_activations: False  # recompute activations and save memory for extra compute
   mel_spec:
     target_sample_rate: 24000
@@ -35,13 +38,14 @@ model:
     hop_length: 256
     win_length: 1024
     n_fft: 1024
-    mel_spec_type: vocos  # 'vocos' or 'bigvgan'
+    mel_spec_type: vocos  # vocos | bigvgan
   vocoder:
     is_local: False  # use local offline ckpt or not
-    local_path: None  # local vocoder path
+    local_path: null  # local vocoder path
 
 ckpts:
-  logger: wandb  # wandb | tensorboard | None
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
   save_per_updates: 50000  # save checkpoint per updates
   keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
   last_per_updates: 5000  # save last checkpoint per updates

src/f5_tts/configs/F5TTS_v1_Base.yaml

@@ -0,0 +1,53 @@
+hydra:
+  run:
+    dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
+
+datasets:
+  name: Emilia_ZH_EN  # dataset name
+  batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
+  batch_size_type: frame  # frame | sample
+  max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
+  num_workers: 16
+
+optim:
+  epochs: 11
+  learning_rate: 7.5e-5
+  num_warmup_updates: 20000  # warmup updates
+  grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
+  max_grad_norm: 1.0  # gradient clipping
+  bnb_optimizer: False  # use bnb 8bit AdamW optimizer or not
+
+model:
+  name: F5TTS_v1_Base  # model name
+  tokenizer: pinyin  # tokenizer type
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: DiT
+  arch:
+    dim: 1024
+    depth: 22
+    heads: 16
+    ff_mult: 2
+    text_dim: 512
+    text_mask_padding: True
+    qk_norm: null  # null | rms_norm
+    conv_layers: 4
+    pe_attn_head: null
+    checkpoint_activations: False  # recompute activations and save memory for extra compute
+  mel_spec:
+    target_sample_rate: 24000
+    n_mel_channels: 100
+    hop_length: 256
+    win_length: 1024
+    n_fft: 1024
+    mel_spec_type: vocos  # vocos | bigvgan
+  vocoder:
+    is_local: False  # use local offline ckpt or not
+    local_path: null  # local vocoder path
+
+ckpts:
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
+  save_per_updates: 50000  # save checkpoint per updates
+  keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+  last_per_updates: 5000  # save last checkpoint per updates
+  save_dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}

src/f5_tts/eval/eval_infer_batch.py

@@ -10,6 +10,7 @@ from importlib.resources import files
 import torch
 import torchaudio
 from accelerate import Accelerator
+from omegaconf import OmegaConf
 from tqdm import tqdm
 
 from f5_tts.eval.utils_eval import (
@@ -18,36 +19,26 @@ from f5_tts.eval.utils_eval import (
     get_seedtts_testset_metainfo,
 )
 from f5_tts.infer.utils_infer import load_checkpoint, load_vocoder
-from f5_tts.model import CFM, DiT, UNetT
+from f5_tts.model import CFM, DiT, UNetT  # noqa: F401. used for config
 from f5_tts.model.utils import get_tokenizer
 
 accelerator = Accelerator()
 device = f"cuda:{accelerator.process_index}"
 
 
-# --------------------- Dataset Settings -------------------- #
-
-target_sample_rate = 24000
-n_mel_channels = 100
-hop_length = 256
-win_length = 1024
-n_fft = 1024
+use_ema = True
 target_rms = 0.1
 
+
 rel_path = str(files("f5_tts").joinpath("../../"))
 
 
 def main():
-    # ---------------------- infer setting ---------------------- #
-
     parser = argparse.ArgumentParser(description="batch inference")
 
     parser.add_argument("-s", "--seed", default=None, type=int)
-    parser.add_argument("-d", "--dataset", default="Emilia_ZH_EN")
     parser.add_argument("-n", "--expname", required=True)
-    parser.add_argument("-c", "--ckptstep", default=1200000, type=int)
-    parser.add_argument("-m", "--mel_spec_type", default="vocos", type=str, choices=["bigvgan", "vocos"])
-    parser.add_argument("-to", "--tokenizer", default="pinyin", type=str, choices=["pinyin", "char"])
+    parser.add_argument("-c", "--ckptstep", default=1250000, type=int)
 
     parser.add_argument("-nfe", "--nfestep", default=32, type=int)
     parser.add_argument("-o", "--odemethod", default="euler")
@@ -58,12 +49,8 @@ def main():
     args = parser.parse_args()
 
     seed = args.seed
-    dataset_name = args.dataset
     exp_name = args.expname
     ckpt_step = args.ckptstep
-    ckpt_path = rel_path + f"/ckpts/{exp_name}/model_{ckpt_step}.pt"
-    mel_spec_type = args.mel_spec_type
-    tokenizer = args.tokenizer
 
     nfe_step = args.nfestep
     ode_method = args.odemethod
@@ -77,13 +64,19 @@ def main():
     use_truth_duration = False
     no_ref_audio = False
 
-    if exp_name == "F5TTS_Base":
-        model_cls = DiT
-        model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+    model_cfg = OmegaConf.load(str(files("f5_tts").joinpath(f"configs/{exp_name}.yaml")))
+    model_cls = globals()[model_cfg.model.backbone]
+    model_arc = model_cfg.model.arch
 
-    elif exp_name == "E2TTS_Base":
-        model_cls = UNetT
-        model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+    dataset_name = model_cfg.datasets.name
+    tokenizer = model_cfg.model.tokenizer
+
+    mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+    target_sample_rate = model_cfg.model.mel_spec.target_sample_rate
+    n_mel_channels = model_cfg.model.mel_spec.n_mel_channels
+    hop_length = model_cfg.model.mel_spec.hop_length
+    win_length = model_cfg.model.mel_spec.win_length
+    n_fft = model_cfg.model.mel_spec.n_fft
 
     if testset == "ls_pc_test_clean":
         metalst = rel_path + "/data/librispeech_pc_test_clean_cross_sentence.lst"
@@ -111,8 +104,6 @@ def main():
 
     # -------------------------------------------------#
 
-    use_ema = True
-
     prompts_all = get_inference_prompt(
         metainfo,
         speed=speed,
@@ -139,7 +130,7 @@ def main():
 
     # Model
     model = CFM(
-        transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+        transformer=model_cls(**model_arc, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
         mel_spec_kwargs=dict(
             n_fft=n_fft,
             hop_length=hop_length,
@@ -154,6 +145,10 @@ def main():
         vocab_char_map=vocab_char_map,
     ).to(device)
 
+    ckpt_path = rel_path + f"/ckpts/{exp_name}/model_{ckpt_step}.pt"
+    if not os.path.exists(ckpt_path):
+        print("Loading from self-organized training checkpoints rather than released pretrained.")
+        ckpt_path = rel_path + f"/{model_cfg.ckpts.save_dir}/model_{ckpt_step}.pt"
     dtype = torch.float32 if mel_spec_type == "bigvgan" else None
     model = load_checkpoint(model, ckpt_path, device, dtype=dtype, use_ema=use_ema)
 

src/f5_tts/eval/eval_infer_batch.sh

@@ -1,13 +1,18 @@
 #!/bin/bash
 
 # e.g. F5-TTS, 16 NFE
-accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_Base" -t "seedtts_test_zh" -nfe 16
-accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_Base" -t "seedtts_test_en" -nfe 16
-accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_Base" -t "ls_pc_test_clean" -nfe 16
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "seedtts_test_zh" -nfe 16
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "seedtts_test_en" -nfe 16
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "ls_pc_test_clean" -nfe 16
 
 # e.g. Vanilla E2 TTS, 32 NFE
-accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -t "seedtts_test_zh" -o "midpoint" -ss 0
-accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -t "seedtts_test_en" -o "midpoint" -ss 0
-accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -t "ls_pc_test_clean" -o "midpoint" -ss 0
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "seedtts_test_zh" -o "midpoint" -ss 0
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "seedtts_test_en" -o "midpoint" -ss 0
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "ls_pc_test_clean" -o "midpoint" -ss 0
+
+# e.g. evaluate F5-TTS 16 NFE result on Seed-TTS test-zh
+python src/f5_tts/eval/eval_seedtts_testset.py -e wer -l zh --gen_wav_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe32_vocos_ss-1_cfg2.0_speed1.0 --gpu_nums 8
+python src/f5_tts/eval/eval_seedtts_testset.py -e sim -l zh --gen_wav_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe32_vocos_ss-1_cfg2.0_speed1.0 --gpu_nums 8
+python src/f5_tts/eval/eval_utmos.py --audio_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe32_vocos_ss-1_cfg2.0_speed1.0
 
 # etc.

src/f5_tts/eval/eval_librispeech_test_clean.py

@@ -53,43 +53,37 @@ def main():
         asr_ckpt_dir = ""  # auto download to cache dir
     wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth"
 
-    # --------------------------- WER ---------------------------
+    # --------------------------------------------------------------------------
 
-    if eval_task == "wer":
-        wer_results = []
-        wers = []
+    full_results = []
+    metrics = []
 
+    if eval_task == "wer":
         with mp.Pool(processes=len(gpus)) as pool:
             args = [(rank, lang, sub_test_set, asr_ckpt_dir) for (rank, sub_test_set) in test_set]
             results = pool.map(run_asr_wer, args)
             for r in results:
-                wer_results.extend(r)
-
-        wer_result_path = f"{gen_wav_dir}/{lang}_wer_results.jsonl"
-        with open(wer_result_path, "w") as f:
-            for line in wer_results:
-                wers.append(line["wer"])
-                json_line = json.dumps(line, ensure_ascii=False)
-                f.write(json_line + "\n")
-
-        wer = round(np.mean(wers) * 100, 3)
-        print(f"\nTotal {len(wers)} samples")
-        print(f"WER      : {wer}%")
-        print(f"Results have been saved to {wer_result_path}")
-
-    # --------------------------- SIM ---------------------------
-
-    if eval_task == "sim":
-        sims = []
+                full_results.extend(r)
+    elif eval_task == "sim":
         with mp.Pool(processes=len(gpus)) as pool:
             args = [(rank, sub_test_set, wavlm_ckpt_dir) for (rank, sub_test_set) in test_set]
             results = pool.map(run_sim, args)
             for r in results:
-                sims.extend(r)
-
-        sim = round(sum(sims) / len(sims), 3)
-        print(f"\nTotal {len(sims)} samples")
-        print(f"SIM      : {sim}")
+                full_results.extend(r)
+    else:
+        raise ValueError(f"Unknown metric type: {eval_task}")
+
+    result_path = f"{gen_wav_dir}/_{eval_task}_results.jsonl"
+    with open(result_path, "w") as f:
+        for line in full_results:
+            metrics.append(line[eval_task])
+            f.write(json.dumps(line, ensure_ascii=False) + "\n")
+        metric = round(np.mean(metrics), 5)
+        f.write(f"\n{eval_task.upper()}: {metric}\n")
+
+    print(f"\nTotal {len(metrics)} samples")
+    print(f"{eval_task.upper()}: {metric}")
+    print(f"{eval_task.upper()} results saved to {result_path}")
 
 
 if __name__ == "__main__":

src/f5_tts/eval/eval_seedtts_testset.py

@@ -52,43 +52,37 @@ def main():
         asr_ckpt_dir = ""  # auto download to cache dir
     wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth"
 
-    # --------------------------- WER ---------------------------
+    # --------------------------------------------------------------------------
 
-    if eval_task == "wer":
-        wer_results = []
-        wers = []
+    full_results = []
+    metrics = []
 
+    if eval_task == "wer":
         with mp.Pool(processes=len(gpus)) as pool:
             args = [(rank, lang, sub_test_set, asr_ckpt_dir) for (rank, sub_test_set) in test_set]
             results = pool.map(run_asr_wer, args)
             for r in results:
-                wer_results.extend(r)
-
-        wer_result_path = f"{gen_wav_dir}/{lang}_wer_results.jsonl"
-        with open(wer_result_path, "w") as f:
-            for line in wer_results:
-                wers.append(line["wer"])
-                json_line = json.dumps(line, ensure_ascii=False)
-                f.write(json_line + "\n")
-
-        wer = round(np.mean(wers) * 100, 3)
-        print(f"\nTotal {len(wers)} samples")
-        print(f"WER      : {wer}%")
-        print(f"Results have been saved to {wer_result_path}")
-
-    # --------------------------- SIM ---------------------------
-
-    if eval_task == "sim":
-        sims = []
+                full_results.extend(r)
+    elif eval_task == "sim":
         with mp.Pool(processes=len(gpus)) as pool:
             args = [(rank, sub_test_set, wavlm_ckpt_dir) for (rank, sub_test_set) in test_set]
             results = pool.map(run_sim, args)
             for r in results:
-                sims.extend(r)
-
-        sim = round(sum(sims) / len(sims), 3)
-        print(f"\nTotal {len(sims)} samples")
-        print(f"SIM      : {sim}")
+                full_results.extend(r)
+    else:
+        raise ValueError(f"Unknown metric type: {eval_task}")
+
+    result_path = f"{gen_wav_dir}/_{eval_task}_results.jsonl"
+    with open(result_path, "w") as f:
+        for line in full_results:
+            metrics.append(line[eval_task])
+            f.write(json.dumps(line, ensure_ascii=False) + "\n")
+        metric = round(np.mean(metrics), 5)
+        f.write(f"\n{eval_task.upper()}: {metric}\n")
+
+    print(f"\nTotal {len(metrics)} samples")
+    print(f"{eval_task.upper()}: {metric}")
+    print(f"{eval_task.upper()} results saved to {result_path}")
 
 
 if __name__ == "__main__":

src/f5_tts/eval/eval_utmos.py

@@ -19,25 +19,23 @@ def main():
     predictor = predictor.to(device)
 
     audio_paths = list(Path(args.audio_dir).rglob(f"*.{args.ext}"))
-    utmos_results = {}
     utmos_score = 0
 
-    for audio_path in tqdm(audio_paths, desc="Processing"):
-        wav_name = audio_path.stem
-        wav, sr = librosa.load(audio_path, sr=None, mono=True)
-        wav_tensor = torch.from_numpy(wav).to(device).unsqueeze(0)
-        score = predictor(wav_tensor, sr)
-        utmos_results[str(wav_name)] = score.item()
-        utmos_score += score.item()
-
-    avg_score = utmos_score / len(audio_paths) if len(audio_paths) > 0 else 0
-    print(f"UTMOS: {avg_score}")
-
-    utmos_result_path = Path(args.audio_dir) / "utmos_results.json"
+    utmos_result_path = Path(args.audio_dir) / "_utmos_results.jsonl"
     with open(utmos_result_path, "w", encoding="utf-8") as f:
-        json.dump(utmos_results, f, ensure_ascii=False, indent=4)
-
-    print(f"Results have been saved to {utmos_result_path}")
+        for audio_path in tqdm(audio_paths, desc="Processing"):
+            wav, sr = librosa.load(audio_path, sr=None, mono=True)
+            wav_tensor = torch.from_numpy(wav).to(device).unsqueeze(0)
+            score = predictor(wav_tensor, sr)
+            line = {}
+            line["wav"], line["utmos"] = str(audio_path.stem), score.item()
+            utmos_score += score.item()
+            f.write(json.dumps(line, ensure_ascii=False) + "\n")
+        avg_score = utmos_score / len(audio_paths) if len(audio_paths) > 0 else 0
+        f.write(f"\nUTMOS: {avg_score:.4f}\n")
+
+    print(f"UTMOS: {avg_score:.4f}")
+    print(f"UTMOS results saved to {utmos_result_path}")
 
 
 if __name__ == "__main__":

src/f5_tts/eval/utils_eval.py

@@ -389,10 +389,10 @@ def run_sim(args):
         model = model.cuda(device)
     model.eval()
 
-    sims = []
-    for wav1, wav2, truth in tqdm(test_set):
-        wav1, sr1 = torchaudio.load(wav1)
-        wav2, sr2 = torchaudio.load(wav2)
+    sim_results = []
+    for gen_wav, prompt_wav, truth in tqdm(test_set):
+        wav1, sr1 = torchaudio.load(gen_wav)
+        wav2, sr2 = torchaudio.load(prompt_wav)
 
         resample1 = torchaudio.transforms.Resample(orig_freq=sr1, new_freq=16000)
         resample2 = torchaudio.transforms.Resample(orig_freq=sr2, new_freq=16000)
@@ -408,6 +408,11 @@ def run_sim(args):
 
         sim = F.cosine_similarity(emb1, emb2)[0].item()
         # print(f"VSim score between two audios: {sim:.4f} (-1.0, 1.0).")
-        sims.append(sim)
+        sim_results.append(
+            {
+                "wav": Path(gen_wav).stem,
+                "sim": sim,
+            }
+        )
 
-    return sims
+    return sim_results

src/f5_tts/infer/README.md

@@ -68,14 +68,16 @@ Basically you can inference with flags:
 ```bash
 # Leave --ref_text "" will have ASR model transcribe (extra GPU memory usage)
 f5-tts_infer-cli \
---model "F5-TTS" \
+--model F5TTS_v1_Base \
 --ref_audio "ref_audio.wav" \
 --ref_text "The content, subtitle or transcription of reference audio." \
 --gen_text "Some text you want TTS model generate for you."
 
-# Choose Vocoder
-f5-tts_infer-cli --vocoder_name bigvgan --load_vocoder_from_local --ckpt_file <YOUR_CKPT_PATH, eg:ckpts/F5TTS_Base_bigvgan/model_1250000.pt>
-f5-tts_infer-cli --vocoder_name vocos --load_vocoder_from_local --ckpt_file <YOUR_CKPT_PATH, eg:ckpts/F5TTS_Base/model_1200000.safetensors>
+# Use BigVGAN as vocoder. Currently only support F5TTS_Base. 
+f5-tts_infer-cli --model F5TTS_Base --vocoder_name bigvgan --load_vocoder_from_local
+
+# Use custom path checkpoint, e.g.
+f5-tts_infer-cli --ckpt_file ckpts/F5TTS_Base/model_1200000.safetensors
 
 # More instructions
 f5-tts_infer-cli --help
@@ -90,8 +92,8 @@ f5-tts_infer-cli -c custom.toml
 For example, you can use `.toml` to pass in variables, refer to `src/f5_tts/infer/examples/basic/basic.toml`:
 
 ```toml
-# F5-TTS | E2-TTS
-model = "F5-TTS"
+# F5TTS_v1_Base | E2TTS_Base
+model = "F5TTS_v1_Base"
 ref_audio = "infer/examples/basic/basic_ref_en.wav"
 # If an empty "", transcribes the reference audio automatically.
 ref_text = "Some call me nature, others call me mother nature."
@@ -105,8 +107,8 @@ output_dir = "tests"
 You can also leverage `.toml` file to do multi-style generation, refer to `src/f5_tts/infer/examples/multi/story.toml`.
 
 ```toml
-# F5-TTS | E2-TTS
-model = "F5-TTS"
+# F5TTS_v1_Base | E2TTS_Base
+model = "F5TTS_v1_Base"
 ref_audio = "infer/examples/multi/main.flac"
 # If an empty "", transcribes the reference audio automatically.
 ref_text = ""
@@ -126,94 +128,27 @@ ref_text = ""
 ```
 You should mark the voice with `[main]` `[town]` `[country]` whenever you want to change voice, refer to `src/f5_tts/infer/examples/multi/story.txt`.
 
-## Speech Editing
-
-To test speech editing capabilities, use the following command:
-
-```bash
-python src/f5_tts/infer/speech_edit.py
-```
+## Socket Real-time Service
 
-## Socket Realtime Client
+Real-time voice output with chunk stream:
 
-To communicate with socket server you need to run 
 ```bash
+# Start socket server
 python src/f5_tts/socket_server.py
-```
-
-<details>
-<summary>Then create client to communicate</summary>
 
-```bash
 # If PyAudio not installed
 sudo apt-get install portaudio19-dev
 pip install pyaudio
-```
-
-``` python
-# Create the socket_client.py
-import socket
-import asyncio
-import pyaudio
-import numpy as np
-import logging
-import time
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-async def listen_to_F5TTS(text, server_ip="localhost", server_port=9998):
-    client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-    await asyncio.get_event_loop().run_in_executor(None, client_socket.connect, (server_ip, int(server_port)))
 
-    start_time = time.time()
-    first_chunk_time = None
-
-    async def play_audio_stream():
-        nonlocal first_chunk_time
-        p = pyaudio.PyAudio()
-        stream = p.open(format=pyaudio.paFloat32, channels=1, rate=24000, output=True, frames_per_buffer=2048)
-
-        try:
-            while True:
-                data = await asyncio.get_event_loop().run_in_executor(None, client_socket.recv, 8192)
-                if not data:
-                    break
-                if data == b"END":
-                    logger.info("End of audio received.")
-                    break
-
-                audio_array = np.frombuffer(data, dtype=np.float32)
-                stream.write(audio_array.tobytes())
-
-                if first_chunk_time is None:
-                    first_chunk_time = time.time()
-
-        finally:
-            stream.stop_stream()
-            stream.close()
-            p.terminate()
-
-        logger.info(f"Total time taken: {time.time() - start_time:.4f} seconds")
-
-    try:
-        data_to_send = f"{text}".encode("utf-8")
-        await asyncio.get_event_loop().run_in_executor(None, client_socket.sendall, data_to_send)
-        await play_audio_stream()
-
-    except Exception as e:
-        logger.error(f"Error in listen_to_F5TTS: {e}")
-
-    finally:
-        client_socket.close()
+# Communicate with socket client
+python src/f5_tts/socket_client.py
+```
 
+## Speech Editing
 
-if __name__ == "__main__":
-    text_to_send = "As a Reader assistant, I'm familiar with new technology. which are key to its improved performance in terms of both training speed and inference efficiency. Let's break down the components"
+To test speech editing capabilities, use the following command:
 
-    asyncio.run(listen_to_F5TTS(text_to_send))
+```bash
+python src/f5_tts/infer/speech_edit.py
 ```
 
-</details>
-

src/f5_tts/infer/SHARED.md

@@ -16,7 +16,7 @@
 <!-- omit in toc -->
 ### Supported Languages
 - [Multilingual](#multilingual)
-    - [F5-TTS Base @ zh \& en @ F5-TTS](#f5-tts-base--zh--en--f5-tts)
+    - [F5-TTS v1 v0 Base @ zh \& en @ F5-TTS](#f5-tts-v1-v0-base--zh--en--f5-tts)
 - [English](#english)
 - [Finnish](#finnish)
     - [F5-TTS Base @ fi @ AsmoKoskinen](#f5-tts-base--fi--asmokoskinen)
@@ -37,7 +37,17 @@
 
 ## Multilingual
 
-#### F5-TTS Base @ zh & en @ F5-TTS
+#### F5-TTS v1 v0 Base @ zh & en @ F5-TTS
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS v1 Base|[ckpt & vocab](https://huggingface.co/SWivid/F5-TTS/tree/main/F5TTS_v1_Base)|[Emilia 95K zh&en](https://huggingface.co/datasets/amphion/Emilia-Dataset/tree/fc71e07)|cc-by-nc-4.0|
+
+```bash
+Model: hf://SWivid/F5-TTS/F5TTS_v1_Base/model_1250000.safetensors
+Vocab: hf://SWivid/F5-TTS/F5TTS_v1_Base/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "conv_layers": 4}
+```
+
 |Model|🤗Hugging Face|Data (Hours)|Model License|
 |:---:|:------------:|:-----------:|:-------------:|
 |F5-TTS Base|[ckpt & vocab](https://huggingface.co/SWivid/F5-TTS/tree/main/F5TTS_Base)|[Emilia 95K zh&en](https://huggingface.co/datasets/amphion/Emilia-Dataset/tree/fc71e07)|cc-by-nc-4.0|
@@ -45,7 +55,7 @@
 ```bash
 Model: hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors
 Vocab: hf://SWivid/F5-TTS/F5TTS_Base/vocab.txt
-Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "conv_layers": 4}
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
 ```
 
 *Other infos, e.g. Author info, Github repo, Link to some sampled results, Usage instruction, Tutorial (Blog, Video, etc.) ...*
@@ -64,7 +74,7 @@ Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "
 ```bash
 Model: hf://AsmoKoskinen/F5-TTS_Finnish_Model/model_common_voice_fi_vox_populi_fi_20241206.safetensors
 Vocab: hf://AsmoKoskinen/F5-TTS_Finnish_Model/vocab.txt
-Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "conv_layers": 4}
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
 ```
 
 
@@ -78,7 +88,7 @@ Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "
 ```bash
 Model: hf://RASPIAUDIO/F5-French-MixedSpeakers-reduced/model_last_reduced.pt
 Vocab: hf://RASPIAUDIO/F5-French-MixedSpeakers-reduced/vocab.txt
-Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "conv_layers": 4}
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
 ```
 
 - [Online Inference with Hugging Face Space](https://huggingface.co/spaces/RASPIAUDIO/f5-tts_french).
@@ -96,7 +106,7 @@ Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "
 ```bash
 Model: hf://SPRINGLab/F5-Hindi-24KHz/model_2500000.safetensors
 Vocab: hf://SPRINGLab/F5-Hindi-24KHz/vocab.txt
-Config: {"dim": 768, "depth": 18, "heads": 12, "ff_mult": 2, "text_dim": 512, "conv_layers": 4}
+Config: {"dim": 768, "depth": 18, "heads": 12, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
 ```
 
 - Authors: SPRING Lab, Indian Institute of Technology, Madras
@@ -113,7 +123,7 @@ Config: {"dim": 768, "depth": 18, "heads": 12, "ff_mult": 2, "text_dim": 512, "c
 ```bash
 Model: hf://alien79/F5-TTS-italian/model_159600.safetensors
 Vocab: hf://alien79/F5-TTS-italian/vocab.txt
-Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "conv_layers": 4}
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
 ```
 
 - Trained by [Mithril Man](https://github.com/MithrilMan)
@@ -131,7 +141,7 @@ Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "
 ```bash
 Model: hf://Jmica/F5TTS/JA_25498980/model_25498980.pt
 Vocab: hf://Jmica/F5TTS/JA_25498980/vocab_updated.txt
-Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "conv_layers": 4}
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
 ```
 
 
@@ -148,7 +158,7 @@ Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "
 ```bash
 Model: hf://hotstone228/F5-TTS-Russian/model_last.safetensors
 Vocab: hf://hotstone228/F5-TTS-Russian/vocab.txt
-Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "conv_layers": 4}
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
 ```
 - Finetuned by [HotDro4illa](https://github.com/HotDro4illa)
 - Any improvements are welcome

src/f5_tts/infer/infer_cli.py

@@ -27,7 +27,7 @@ from f5_tts.infer.utils_infer import (
     preprocess_ref_audio_text,
     remove_silence_for_generated_wav,
 )
-from f5_tts.model import DiT, UNetT
+from f5_tts.model import DiT, UNetT  # noqa: F401. used for config
 
 
 parser = argparse.ArgumentParser(
@@ -50,7 +50,7 @@ parser.add_argument(
     "-m",
     "--model",
     type=str,
-    help="The model name: F5-TTS | E2-TTS",
+    help="The model name: F5TTS_v1_Base | F5TTS_Base | E2TTS_Base | etc.",
 )
 parser.add_argument(
     "-mc",
@@ -172,8 +172,7 @@ config = tomli.load(open(args.config, "rb"))
 
 # command-line interface parameters
 
-model = args.model or config.get("model", "F5-TTS")
-model_cfg = args.model_cfg or config.get("model_cfg", str(files("f5_tts").joinpath("configs/F5TTS_Base_train.yaml")))
+model = args.model or config.get("model", "F5TTS_v1_Base")
 ckpt_file = args.ckpt_file or config.get("ckpt_file", "")
 vocab_file = args.vocab_file or config.get("vocab_file", "")
 
@@ -245,36 +244,32 @@ vocoder = load_vocoder(vocoder_name=vocoder_name, is_local=load_vocoder_from_loc
 
 # load TTS model
 
-if model == "F5-TTS":
-    model_cls = DiT
-    model_cfg = OmegaConf.load(model_cfg).model.arch
-    if not ckpt_file:  # path not specified, download from repo
-        if vocoder_name == "vocos":
-            repo_name = "F5-TTS"
-            exp_name = "F5TTS_Base"
-            ckpt_step = 1200000
-            ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
-            # ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt"  # .pt | .safetensors; local path
-        elif vocoder_name == "bigvgan":
-            repo_name = "F5-TTS"
-            exp_name = "F5TTS_Base_bigvgan"
-            ckpt_step = 1250000
-            ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.pt"))
-
-elif model == "E2-TTS":
-    assert args.model_cfg is None, "E2-TTS does not support custom model_cfg yet"
-    assert vocoder_name == "vocos", "E2-TTS only supports vocoder vocos yet"
-    model_cls = UNetT
-    model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
-    if not ckpt_file:  # path not specified, download from repo
-        repo_name = "E2-TTS"
-        exp_name = "E2TTS_Base"
+model_cfg = OmegaConf.load(
+    args.model_cfg or config.get("model_cfg", str(files("f5_tts").joinpath(f"configs/{model}.yaml")))
+).model
+model_cls = globals()[model_cfg.backbone]
+
+repo_name, ckpt_step, ckpt_type = "F5-TTS", 1250000, "safetensors"
+
+if model != "F5TTS_Base":
+    assert vocoder_name == model_cfg.mel_spec.mel_spec_type
+
+# override for previous models
+if model == "F5TTS_Base":
+    if vocoder_name == "vocos":
         ckpt_step = 1200000
-        ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
-        # ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt"  # .pt | .safetensors; local path
+    elif vocoder_name == "bigvgan":
+        model = "F5TTS_Base_bigvgan"
+        ckpt_type = "pt"
+elif model == "E2TTS_Base":
+    repo_name = "E2-TTS"
+    ckpt_step = 1200000
+
+if not ckpt_file:
+    ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{model}/model_{ckpt_step}.{ckpt_type}"))
 
 print(f"Using {model}...")
-ema_model = load_model(model_cls, model_cfg, ckpt_file, mel_spec_type=vocoder_name, vocab_file=vocab_file)
+ema_model = load_model(model_cls, model_cfg.arch, ckpt_file, mel_spec_type=vocoder_name, vocab_file=vocab_file)
 
 
 # inference process

src/f5_tts/infer/infer_gradio.py

@@ -41,12 +41,12 @@ from f5_tts.infer.utils_infer import (
 )
 
 
-DEFAULT_TTS_MODEL = "F5-TTS"
+DEFAULT_TTS_MODEL = "F5-TTS_v1"
 tts_model_choice = DEFAULT_TTS_MODEL
 
 DEFAULT_TTS_MODEL_CFG = [
-    "hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors",
-    "hf://SWivid/F5-TTS/F5TTS_Base/vocab.txt",
+    "hf://SWivid/F5-TTS/F5TTS_v1_Base/model_1250000.safetensors",
+    "hf://SWivid/F5-TTS/F5TTS_v1_Base/vocab.txt",
     json.dumps(dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)),
 ]
 
@@ -56,13 +56,15 @@ DEFAULT_TTS_MODEL_CFG = [
 vocoder = load_vocoder()
 
 
-def load_f5tts(ckpt_path=str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors"))):
-    F5TTS_model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+def load_f5tts():
+    ckpt_path = str(cached_path(DEFAULT_TTS_MODEL_CFG[0]))
+    F5TTS_model_cfg = json.loads(DEFAULT_TTS_MODEL_CFG[2])
     return load_model(DiT, F5TTS_model_cfg, ckpt_path)
 
 
-def load_e2tts(ckpt_path=str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors"))):
-    E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+def load_e2tts():
+    ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors"))
+    E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4, text_mask_padding=False, pe_attn_head=1)
     return load_model(UNetT, E2TTS_model_cfg, ckpt_path)
 
 
@@ -73,7 +75,7 @@ def load_custom(ckpt_path: str, vocab_path="", model_cfg=None):
     if vocab_path.startswith("hf://"):
         vocab_path = str(cached_path(vocab_path))
     if model_cfg is None:
-        model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+        model_cfg = json.loads(DEFAULT_TTS_MODEL_CFG[2])
     return load_model(DiT, model_cfg, ckpt_path, vocab_file=vocab_path)
 
 
@@ -130,7 +132,7 @@ def infer(
 
     ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=show_info)
 
-    if model == "F5-TTS":
+    if model == DEFAULT_TTS_MODEL:
         ema_model = F5TTS_ema_model
     elif model == "E2-TTS":
         global E2TTS_ema_model
@@ -762,7 +764,7 @@ If you're having issues, try converting your reference audio to WAV or MP3, clip
 """
     )
 
-    last_used_custom = files("f5_tts").joinpath("infer/.cache/last_used_custom_model_info.txt")
+    last_used_custom = files("f5_tts").joinpath("infer/.cache/last_used_custom_model_info_v1.txt")
 
     def load_last_used_custom():
         try:
@@ -821,7 +823,30 @@ If you're having issues, try converting your reference audio to WAV or MP3, clip
         custom_model_cfg = gr.Dropdown(
             choices=[
                 DEFAULT_TTS_MODEL_CFG[2],
-                json.dumps(dict(dim=768, depth=18, heads=12, ff_mult=2, text_dim=512, conv_layers=4)),
+                json.dumps(
+                    dict(
+                        dim=1024,
+                        depth=22,
+                        heads=16,
+                        ff_mult=2,
+                        text_dim=512,
+                        text_mask_padding=False,
+                        conv_layers=4,
+                        pe_attn_head=1,
+                    )
+                ),
+                json.dumps(
+                    dict(
+                        dim=768,
+                        depth=18,
+                        heads=12,
+                        ff_mult=2,
+                        text_dim=512,
+                        text_mask_padding=False,
+                        conv_layers=4,
+                        pe_attn_head=1,
+                    )
+                ),
             ],
             value=load_last_used_custom()[2],
             allow_custom_value=True,

src/f5_tts/infer/speech_edit.py

@@ -2,12 +2,15 @@ import os
 
 os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # for MPS device compatibility
 
+from importlib.resources import files
+
 import torch
 import torch.nn.functional as F
 import torchaudio
+from omegaconf import OmegaConf
 
 from f5_tts.infer.utils_infer import load_checkpoint, load_vocoder, save_spectrogram
-from f5_tts.model import CFM, DiT, UNetT
+from f5_tts.model import CFM, DiT, UNetT  # noqa: F401. used for config
 from f5_tts.model.utils import convert_char_to_pinyin, get_tokenizer
 
 device = (
@@ -21,44 +24,40 @@ device = (
 )
 
 
-# --------------------- Dataset Settings -------------------- #
-
-target_sample_rate = 24000
-n_mel_channels = 100
-hop_length = 256
-win_length = 1024
-n_fft = 1024
-mel_spec_type = "vocos"  # 'vocos' or 'bigvgan'
-target_rms = 0.1
-
-tokenizer = "pinyin"
-dataset_name = "Emilia_ZH_EN"
-
-
 # ---------------------- infer setting ---------------------- #
 
 seed = None  # int | None
 
-exp_name = "F5TTS_Base"  # F5TTS_Base | E2TTS_Base
-ckpt_step = 1200000
+exp_name = "F5TTS_v1_Base"  # F5TTS_v1_Base | E2TTS_Base
+ckpt_step = 1250000
 
 nfe_step = 32  # 16, 32
 cfg_strength = 2.0
 ode_method = "euler"  # euler | midpoint
 sway_sampling_coef = -1.0
 speed = 1.0
+target_rms = 0.1
+
+
+model_cfg = OmegaConf.load(str(files("f5_tts").joinpath(f"configs/{exp_name}.yaml")))
+model_cls = globals()[model_cfg.model.backbone]
+model_arc = model_cfg.model.arch
 
-if exp_name == "F5TTS_Base":
-    model_cls = DiT
-    model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+dataset_name = model_cfg.datasets.name
+tokenizer = model_cfg.model.tokenizer
 
-elif exp_name == "E2TTS_Base":
-    model_cls = UNetT
-    model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+target_sample_rate = model_cfg.model.mel_spec.target_sample_rate
+n_mel_channels = model_cfg.model.mel_spec.n_mel_channels
+hop_length = model_cfg.model.mel_spec.hop_length
+win_length = model_cfg.model.mel_spec.win_length
+n_fft = model_cfg.model.mel_spec.n_fft
 
-ckpt_path = f"ckpts/{exp_name}/model_{ckpt_step}.safetensors"
+
+ckpt_path = str(files("f5_tts").joinpath("../../")) + f"ckpts/{exp_name}/model_{ckpt_step}.safetensors"
 output_dir = "tests"
 
+
 # [leverage https://github.com/MahmoudAshraf97/ctc-forced-aligner to get char level alignment]
 # pip install git+https://github.com/MahmoudAshraf97/ctc-forced-aligner.git
 # [write the origin_text into a file, e.g. tests/test_edit.txt]
@@ -67,7 +66,7 @@ output_dir = "tests"
 # [--language "zho" for Chinese, "eng" for English]
 # [if local ckpt, set --alignment_model "../checkpoints/mms-300m-1130-forced-aligner"]
 
-audio_to_edit = "src/f5_tts/infer/examples/basic/basic_ref_en.wav"
+audio_to_edit = str(files("f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav"))
 origin_text = "Some call me nature, others call me mother nature."
 target_text = "Some call me optimist, others call me realist."
 parts_to_edit = [
@@ -106,7 +105,7 @@ vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer)
 
 # Model
 model = CFM(
-    transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+    transformer=model_cls(**model_arc, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
     mel_spec_kwargs=dict(
         n_fft=n_fft,
         hop_length=hop_length,

src/f5_tts/infer/utils_infer.py

@@ -301,19 +301,19 @@ def preprocess_ref_audio_text(ref_audio_orig, ref_text, clip_short=True, show_in
             )
             non_silent_wave = AudioSegment.silent(duration=0)
             for non_silent_seg in non_silent_segs:
-                if len(non_silent_wave) > 6000 and len(non_silent_wave + non_silent_seg) > 15000:
+                if len(non_silent_wave) > 6000 and len(non_silent_wave + non_silent_seg) > 12000:
                     show_info("Audio is over 15s, clipping short. (1)")
                     break
                 non_silent_wave += non_silent_seg
 
             # 2. try to find short silence for clipping if 1. failed
-            if len(non_silent_wave) > 15000:
+            if len(non_silent_wave) > 12000:
                 non_silent_segs = silence.split_on_silence(
                     aseg, min_silence_len=100, silence_thresh=-40, keep_silence=1000, seek_step=10
                 )
                 non_silent_wave = AudioSegment.silent(duration=0)
                 for non_silent_seg in non_silent_segs:
-                    if len(non_silent_wave) > 6000 and len(non_silent_wave + non_silent_seg) > 15000:
+                    if len(non_silent_wave) > 6000 and len(non_silent_wave + non_silent_seg) > 12000:
                         show_info("Audio is over 15s, clipping short. (2)")
                         break
                     non_silent_wave += non_silent_seg
@@ -321,8 +321,8 @@ def preprocess_ref_audio_text(ref_audio_orig, ref_text, clip_short=True, show_in
             aseg = non_silent_wave
 
             # 3. if no proper silence found for clipping
-            if len(aseg) > 15000:
-                aseg = aseg[:15000]
+            if len(aseg) > 12000:
+                aseg = aseg[:12000]
                 show_info("Audio is over 15s, clipping short. (3)")
 
         aseg = remove_silence_edges(aseg) + AudioSegment.silent(duration=50)
@@ -383,7 +383,7 @@ def infer_process(
 ):
     # Split the input text into batches
     audio, sr = torchaudio.load(ref_audio)
-    max_chars = int(len(ref_text.encode("utf-8")) / (audio.shape[-1] / sr) * (25 - audio.shape[-1] / sr))
+    max_chars = int(len(ref_text.encode("utf-8")) / (audio.shape[-1] / sr) * (22 - audio.shape[-1] / sr))
     gen_text_batches = chunk_text(gen_text, max_chars=max_chars)
     for i, gen_text in enumerate(gen_text_batches):
         print(f"gen_text {i}", gen_text)

src/f5_tts/model/backbones/README.md

@@ -4,7 +4,7 @@
 ### unett.py
 - flat unet transformer
 - structure same as in e2-tts & voicebox paper except using rotary pos emb
-- update: allow possible abs pos emb & convnextv2 blocks for embedded text before concat
+- possible abs pos emb & convnextv2 blocks for embedded text before concat
 
 ### dit.py
 - adaln-zero dit
@@ -14,7 +14,7 @@
 - possible long skip connection (first layer to last layer)
 
 ### mmdit.py
-- sd3 structure
+- stable diffusion 3 block structure
 - timestep as condition
 - left stream: text embedded and applied a abs pos emb
 - right stream: masked_cond & noised_input concatted and with same conv pos emb as unett

src/f5_tts/model/backbones/dit.py

@@ -20,7 +20,7 @@ from f5_tts.model.modules import (
     ConvNeXtV2Block,
     ConvPositionEmbedding,
     DiTBlock,
-    AdaLayerNormZero_Final,
+    AdaLayerNorm_Final,
     precompute_freqs_cis,
     get_pos_embed_indices,
 )
@@ -30,10 +30,12 @@ from f5_tts.model.modules import (
 
 
 class TextEmbedding(nn.Module):
-    def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2):
+    def __init__(self, text_num_embeds, text_dim, mask_padding=True, conv_layers=0, conv_mult=2):
         super().__init__()
         self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
 
+        self.mask_padding = mask_padding  # mask filler and batch padding tokens or not
+
         if conv_layers > 0:
             self.extra_modeling = True
             self.precompute_max_pos = 4096  # ~44s of 24khz audio
@@ -49,6 +51,8 @@ class TextEmbedding(nn.Module):
         text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
         batch, text_len = text.shape[0], text.shape[1]
         text = F.pad(text, (0, seq_len - text_len), value=0)
+        if self.mask_padding:
+            text_mask = text == 0
 
         if drop_text:  # cfg for text
             text = torch.zeros_like(text)
@@ -64,7 +68,13 @@ class TextEmbedding(nn.Module):
             text = text + text_pos_embed
 
             # convnextv2 blocks
-            text = self.text_blocks(text)
+            if self.mask_padding:
+                text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+                for block in self.text_blocks:
+                    text = block(text)
+                    text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+            else:
+                text = self.text_blocks(text)
 
         return text
 
@@ -103,7 +113,10 @@ class DiT(nn.Module):
         mel_dim=100,
         text_num_embeds=256,
         text_dim=None,
+        text_mask_padding=True,
+        qk_norm=None,
         conv_layers=0,
+        pe_attn_head=None,
         long_skip_connection=False,
         checkpoint_activations=False,
     ):
@@ -112,7 +125,10 @@ class DiT(nn.Module):
         self.time_embed = TimestepEmbedding(dim)
         if text_dim is None:
             text_dim = mel_dim
-        self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers=conv_layers)
+        self.text_embed = TextEmbedding(
+            text_num_embeds, text_dim, mask_padding=text_mask_padding, conv_layers=conv_layers
+        )
+        self.text_cond, self.text_uncond = None, None  # text cache
         self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
 
         self.rotary_embed = RotaryEmbedding(dim_head)
@@ -121,15 +137,40 @@ class DiT(nn.Module):
         self.depth = depth
 
         self.transformer_blocks = nn.ModuleList(
-            [DiTBlock(dim=dim, heads=heads, dim_head=dim_head, ff_mult=ff_mult, dropout=dropout) for _ in range(depth)]
+            [
+                DiTBlock(
+                    dim=dim,
+                    heads=heads,
+                    dim_head=dim_head,
+                    ff_mult=ff_mult,
+                    dropout=dropout,
+                    qk_norm=qk_norm,
+                    pe_attn_head=pe_attn_head,
+                )
+                for _ in range(depth)
+            ]
         )
         self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None
 
-        self.norm_out = AdaLayerNormZero_Final(dim)  # final modulation
+        self.norm_out = AdaLayerNorm_Final(dim)  # final modulation
         self.proj_out = nn.Linear(dim, mel_dim)
 
         self.checkpoint_activations = checkpoint_activations
 
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        # Zero-out AdaLN layers in DiT blocks:
+        for block in self.transformer_blocks:
+            nn.init.constant_(block.attn_norm.linear.weight, 0)
+            nn.init.constant_(block.attn_norm.linear.bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.norm_out.linear.weight, 0)
+        nn.init.constant_(self.norm_out.linear.bias, 0)
+        nn.init.constant_(self.proj_out.weight, 0)
+        nn.init.constant_(self.proj_out.bias, 0)
+
     def ckpt_wrapper(self, module):
         # https://github.com/chuanyangjin/fast-DiT/blob/main/models.py
         def ckpt_forward(*inputs):
@@ -138,6 +179,9 @@ class DiT(nn.Module):
 
         return ckpt_forward
 
+    def clear_cache(self):
+        self.text_cond, self.text_uncond = None, None
+
     def forward(
         self,
         x: float["b n d"],  # nosied input audio  # noqa: F722
@@ -147,14 +191,25 @@ class DiT(nn.Module):
         drop_audio_cond,  # cfg for cond audio
         drop_text,  # cfg for text
         mask: bool["b n"] | None = None,  # noqa: F722
+        cache=False,
     ):
         batch, seq_len = x.shape[0], x.shape[1]
         if time.ndim == 0:
             time = time.repeat(batch)
 
-        # t: conditioning time, c: context (text + masked cond audio), x: noised input audio
+        # t: conditioning time, text: text, x: noised audio + cond audio + text
         t = self.time_embed(time)
-        text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
+        if cache:
+            if drop_text:
+                if self.text_uncond is None:
+                    self.text_uncond = self.text_embed(text, seq_len, drop_text=True)
+                text_embed = self.text_uncond
+            else:
+                if self.text_cond is None:
+                    self.text_cond = self.text_embed(text, seq_len, drop_text=False)
+                text_embed = self.text_cond
+        else:
+            text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
         x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
 
         rope = self.rotary_embed.forward_from_seq_len(seq_len)

src/f5_tts/model/backbones/mmdit.py

@@ -18,7 +18,7 @@ from f5_tts.model.modules import (
     TimestepEmbedding,
     ConvPositionEmbedding,
     MMDiTBlock,
-    AdaLayerNormZero_Final,
+    AdaLayerNorm_Final,
     precompute_freqs_cis,
     get_pos_embed_indices,
 )
@@ -28,18 +28,24 @@ from f5_tts.model.modules import (
 
 
 class TextEmbedding(nn.Module):
-    def __init__(self, out_dim, text_num_embeds):
+    def __init__(self, out_dim, text_num_embeds, mask_padding=True):
         super().__init__()
         self.text_embed = nn.Embedding(text_num_embeds + 1, out_dim)  # will use 0 as filler token
 
+        self.mask_padding = mask_padding  # mask filler and batch padding tokens or not
+
         self.precompute_max_pos = 1024
         self.register_buffer("freqs_cis", precompute_freqs_cis(out_dim, self.precompute_max_pos), persistent=False)
 
     def forward(self, text: int["b nt"], drop_text=False) -> int["b nt d"]:  # noqa: F722
-        text = text + 1
-        if drop_text:
+        text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
+        if self.mask_padding:
+            text_mask = text == 0
+
+        if drop_text:  # cfg for text
             text = torch.zeros_like(text)
-        text = self.text_embed(text)
+
+        text = self.text_embed(text)  # b nt -> b nt d
 
         # sinus pos emb
         batch_start = torch.zeros((text.shape[0],), dtype=torch.long)
@@ -49,6 +55,9 @@ class TextEmbedding(nn.Module):
 
         text = text + text_pos_embed
 
+        if self.mask_padding:
+            text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+
         return text
 
 
@@ -83,13 +92,16 @@ class MMDiT(nn.Module):
         dim_head=64,
         dropout=0.1,
         ff_mult=4,
-        text_num_embeds=256,
         mel_dim=100,
+        text_num_embeds=256,
+        text_mask_padding=True,
+        qk_norm=None,
     ):
         super().__init__()
 
         self.time_embed = TimestepEmbedding(dim)
-        self.text_embed = TextEmbedding(dim, text_num_embeds)
+        self.text_embed = TextEmbedding(dim, text_num_embeds, mask_padding=text_mask_padding)
+        self.text_cond, self.text_uncond = None, None  # text cache
         self.audio_embed = AudioEmbedding(mel_dim, dim)
 
         self.rotary_embed = RotaryEmbedding(dim_head)
@@ -106,13 +118,33 @@ class MMDiT(nn.Module):
                     dropout=dropout,
                     ff_mult=ff_mult,
                     context_pre_only=i == depth - 1,
+                    qk_norm=qk_norm,
                 )
                 for i in range(depth)
             ]
         )
-        self.norm_out = AdaLayerNormZero_Final(dim)  # final modulation
+        self.norm_out = AdaLayerNorm_Final(dim)  # final modulation
         self.proj_out = nn.Linear(dim, mel_dim)
 
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        # Zero-out AdaLN layers in MMDiT blocks:
+        for block in self.transformer_blocks:
+            nn.init.constant_(block.attn_norm_x.linear.weight, 0)
+            nn.init.constant_(block.attn_norm_x.linear.bias, 0)
+            nn.init.constant_(block.attn_norm_c.linear.weight, 0)
+            nn.init.constant_(block.attn_norm_c.linear.bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.norm_out.linear.weight, 0)
+        nn.init.constant_(self.norm_out.linear.bias, 0)
+        nn.init.constant_(self.proj_out.weight, 0)
+        nn.init.constant_(self.proj_out.bias, 0)
+
+    def clear_cache(self):
+        self.text_cond, self.text_uncond = None, None
+
     def forward(
         self,
         x: float["b n d"],  # nosied input audio  # noqa: F722
@@ -122,6 +154,7 @@ class MMDiT(nn.Module):
         drop_audio_cond,  # cfg for cond audio
         drop_text,  # cfg for text
         mask: bool["b n"] | None = None,  # noqa: F722
+        cache=False,
     ):
         batch = x.shape[0]
         if time.ndim == 0:
@@ -129,7 +162,17 @@ class MMDiT(nn.Module):
 
         # t: conditioning (time), c: context (text + masked cond audio), x: noised input audio
         t = self.time_embed(time)
-        c = self.text_embed(text, drop_text=drop_text)
+        if cache:
+            if drop_text:
+                if self.text_uncond is None:
+                    self.text_uncond = self.text_embed(text, drop_text=True)
+                c = self.text_uncond
+            else:
+                if self.text_cond is None:
+                    self.text_cond = self.text_embed(text, drop_text=False)
+                c = self.text_cond
+        else:
+            c = self.text_embed(text, drop_text=drop_text)
         x = self.audio_embed(x, cond, drop_audio_cond=drop_audio_cond)
 
         seq_len = x.shape[1]

src/f5_tts/model/backbones/unett.py

@@ -33,10 +33,12 @@ from f5_tts.model.modules import (
 
 
 class TextEmbedding(nn.Module):
-    def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2):
+    def __init__(self, text_num_embeds, text_dim, mask_padding=True, conv_layers=0, conv_mult=2):
         super().__init__()
         self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
 
+        self.mask_padding = mask_padding  # mask filler and batch padding tokens or not
+
         if conv_layers > 0:
             self.extra_modeling = True
             self.precompute_max_pos = 4096  # ~44s of 24khz audio
@@ -52,6 +54,8 @@ class TextEmbedding(nn.Module):
         text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
         batch, text_len = text.shape[0], text.shape[1]
         text = F.pad(text, (0, seq_len - text_len), value=0)
+        if self.mask_padding:
+            text_mask = text == 0
 
         if drop_text:  # cfg for text
             text = torch.zeros_like(text)
@@ -67,7 +71,13 @@ class TextEmbedding(nn.Module):
             text = text + text_pos_embed
 
             # convnextv2 blocks
-            text = self.text_blocks(text)
+            if self.mask_padding:
+                text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+                for block in self.text_blocks:
+                    text = block(text)
+                    text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+            else:
+                text = self.text_blocks(text)
 
         return text
 
@@ -106,7 +116,10 @@ class UNetT(nn.Module):
         mel_dim=100,
         text_num_embeds=256,
         text_dim=None,
+        text_mask_padding=True,
+        qk_norm=None,
         conv_layers=0,
+        pe_attn_head=None,
         skip_connect_type: Literal["add", "concat", "none"] = "concat",
     ):
         super().__init__()
@@ -115,7 +128,10 @@ class UNetT(nn.Module):
         self.time_embed = TimestepEmbedding(dim)
         if text_dim is None:
             text_dim = mel_dim
-        self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers=conv_layers)
+        self.text_embed = TextEmbedding(
+            text_num_embeds, text_dim, mask_padding=text_mask_padding, conv_layers=conv_layers
+        )
+        self.text_cond, self.text_uncond = None, None  # text cache
         self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
 
         self.rotary_embed = RotaryEmbedding(dim_head)
@@ -134,11 +150,12 @@ class UNetT(nn.Module):
 
             attn_norm = RMSNorm(dim)
             attn = Attention(
-                processor=AttnProcessor(),
+                processor=AttnProcessor(pe_attn_head=pe_attn_head),
                 dim=dim,
                 heads=heads,
                 dim_head=dim_head,
                 dropout=dropout,
+                qk_norm=qk_norm,
             )
 
             ff_norm = RMSNorm(dim)
@@ -161,6 +178,9 @@ class UNetT(nn.Module):
         self.norm_out = RMSNorm(dim)
         self.proj_out = nn.Linear(dim, mel_dim)
 
+    def clear_cache(self):
+        self.text_cond, self.text_uncond = None, None
+
     def forward(
         self,
         x: float["b n d"],  # nosied input audio  # noqa: F722
@@ -170,6 +190,7 @@ class UNetT(nn.Module):
         drop_audio_cond,  # cfg for cond audio
         drop_text,  # cfg for text
         mask: bool["b n"] | None = None,  # noqa: F722
+        cache=False,
     ):
         batch, seq_len = x.shape[0], x.shape[1]
         if time.ndim == 0:
@@ -177,7 +198,17 @@ class UNetT(nn.Module):
 
         # t: conditioning time, c: context (text + masked cond audio), x: noised input audio
         t = self.time_embed(time)
-        text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
+        if cache:
+            if drop_text:
+                if self.text_uncond is None:
+                    self.text_uncond = self.text_embed(text, seq_len, drop_text=True)
+                text_embed = self.text_uncond
+            else:
+                if self.text_cond is None:
+                    self.text_cond = self.text_embed(text, seq_len, drop_text=False)
+                text_embed = self.text_cond
+        else:
+            text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
         x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
 
         # postfix time t to input x, [b n d] -> [b n+1 d]

src/f5_tts/model/cfm.py

@@ -162,13 +162,13 @@ class CFM(nn.Module):
 
             # predict flow
             pred = self.transformer(
-                x=x, cond=step_cond, text=text, time=t, mask=mask, drop_audio_cond=False, drop_text=False
+                x=x, cond=step_cond, text=text, time=t, mask=mask, drop_audio_cond=False, drop_text=False, cache=True
             )
             if cfg_strength < 1e-5:
                 return pred
 
             null_pred = self.transformer(
-                x=x, cond=step_cond, text=text, time=t, mask=mask, drop_audio_cond=True, drop_text=True
+                x=x, cond=step_cond, text=text, time=t, mask=mask, drop_audio_cond=True, drop_text=True, cache=True
             )
             return pred + (pred - null_pred) * cfg_strength
 
@@ -195,6 +195,7 @@ class CFM(nn.Module):
             t = t + sway_sampling_coef * (torch.cos(torch.pi / 2 * t) - 1 + t)
 
         trajectory = odeint(fn, y0, t, **self.odeint_kwargs)
+        self.transformer.clear_cache()
 
         sampled = trajectory[-1]
         out = sampled

src/f5_tts/model/dataset.py

@@ -173,7 +173,7 @@ class DynamicBatchSampler(Sampler[list[int]]):
     """
 
     def __init__(
-        self, sampler: Sampler[int], frames_threshold: int, max_samples=0, random_seed=None, drop_last: bool = False
+        self, sampler: Sampler[int], frames_threshold: int, max_samples=0, random_seed=None, drop_residual: bool = False
     ):
         self.sampler = sampler
         self.frames_threshold = frames_threshold
@@ -208,12 +208,15 @@ class DynamicBatchSampler(Sampler[list[int]]):
                     batch = []
                     batch_frames = 0
 
-        if not drop_last and len(batch) > 0:
+        if not drop_residual and len(batch) > 0:
             batches.append(batch)
 
         del indices
         self.batches = batches
 
+        # Ensure even batches with accelerate BatchSamplerShard cls under frame_per_batch setting
+        self.drop_last = True
+
     def set_epoch(self, epoch: int) -> None:
         """Sets the epoch for this sampler."""
         self.epoch = epoch

src/f5_tts/model/modules.py

@@ -269,11 +269,36 @@ class ConvNeXtV2Block(nn.Module):
         return residual + x
 
 
-# AdaLayerNormZero
+# RMSNorm
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.native_rms_norm = float(torch.__version__[:3]) >= 2.4
+
+    def forward(self, x):
+        if self.native_rms_norm:
+            if self.weight.dtype in [torch.float16, torch.bfloat16]:
+                x = x.to(self.weight.dtype)
+            x = F.rms_norm(x, normalized_shape=(x.shape[-1],), weight=self.weight, eps=self.eps)
+        else:
+            variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
+            x = x * torch.rsqrt(variance + self.eps)
+            if self.weight.dtype in [torch.float16, torch.bfloat16]:
+                x = x.to(self.weight.dtype)
+            x = x * self.weight
+
+        return x
+
+
+# AdaLayerNorm
 # return with modulated x for attn input, and params for later mlp modulation
 
 
-class AdaLayerNormZero(nn.Module):
+class AdaLayerNorm(nn.Module):
     def __init__(self, dim):
         super().__init__()
 
@@ -290,11 +315,11 @@ class AdaLayerNormZero(nn.Module):
         return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
 
 
-# AdaLayerNormZero for final layer
+# AdaLayerNorm for final layer
 # return only with modulated x for attn input, cuz no more mlp modulation
 
 
-class AdaLayerNormZero_Final(nn.Module):
+class AdaLayerNorm_Final(nn.Module):
     def __init__(self, dim):
         super().__init__()
 
@@ -341,7 +366,8 @@ class Attention(nn.Module):
         dim_head: int = 64,
         dropout: float = 0.0,
         context_dim: Optional[int] = None,  # if not None -> joint attention
-        context_pre_only=None,
+        context_pre_only: bool = False,
+        qk_norm: Optional[str] = None,
     ):
         super().__init__()
 
@@ -362,18 +388,32 @@ class Attention(nn.Module):
         self.to_k = nn.Linear(dim, self.inner_dim)
         self.to_v = nn.Linear(dim, self.inner_dim)
 
+        if qk_norm is None:
+            self.q_norm = None
+            self.k_norm = None
+        elif qk_norm == "rms_norm":
+            self.q_norm = RMSNorm(dim_head, eps=1e-6)
+            self.k_norm = RMSNorm(dim_head, eps=1e-6)
+        else:
+            raise ValueError(f"Unimplemented qk_norm: {qk_norm}")
+
         if self.context_dim is not None:
+            self.to_q_c = nn.Linear(context_dim, self.inner_dim)
             self.to_k_c = nn.Linear(context_dim, self.inner_dim)
             self.to_v_c = nn.Linear(context_dim, self.inner_dim)
-            if self.context_pre_only is not None:
-                self.to_q_c = nn.Linear(context_dim, self.inner_dim)
+            if qk_norm is None:
+                self.c_q_norm = None
+                self.c_k_norm = None
+            elif qk_norm == "rms_norm":
+                self.c_q_norm = RMSNorm(dim_head, eps=1e-6)
+                self.c_k_norm = RMSNorm(dim_head, eps=1e-6)
 
         self.to_out = nn.ModuleList([])
         self.to_out.append(nn.Linear(self.inner_dim, dim))
         self.to_out.append(nn.Dropout(dropout))
 
-        if self.context_pre_only is not None and not self.context_pre_only:
-            self.to_out_c = nn.Linear(self.inner_dim, dim)
+        if self.context_dim is not None and not self.context_pre_only:
+            self.to_out_c = nn.Linear(self.inner_dim, context_dim)
 
     def forward(
         self,
@@ -393,8 +433,11 @@ class Attention(nn.Module):
 
 
 class AttnProcessor:
-    def __init__(self):
-        pass
+    def __init__(
+        self,
+        pe_attn_head: int | None = None,  # number of attention head to apply rope, None for all
+    ):
+        self.pe_attn_head = pe_attn_head
 
     def __call__(
         self,
@@ -405,19 +448,11 @@ class AttnProcessor:
     ) -> torch.FloatTensor:
         batch_size = x.shape[0]
 
-        # `sample` projections.
+        # `sample` projections
         query = attn.to_q(x)
         key = attn.to_k(x)
         value = attn.to_v(x)
 
-        # apply rotary position embedding
-        if rope is not None:
-            freqs, xpos_scale = rope
-            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
-
-            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
-            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
-
         # attention
         inner_dim = key.shape[-1]
         head_dim = inner_dim // attn.heads
@@ -425,6 +460,25 @@ class AttnProcessor:
         key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
         value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
 
+        # qk norm
+        if attn.q_norm is not None:
+            query = attn.q_norm(query)
+        if attn.k_norm is not None:
+            key = attn.k_norm(key)
+
+        # apply rotary position embedding
+        if rope is not None:
+            freqs, xpos_scale = rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+
+            if self.pe_attn_head is not None:
+                pn = self.pe_attn_head
+                query[:, :pn, :, :] = apply_rotary_pos_emb(query[:, :pn, :, :], freqs, q_xpos_scale)
+                key[:, :pn, :, :] = apply_rotary_pos_emb(key[:, :pn, :, :], freqs, k_xpos_scale)
+            else:
+                query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
+                key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
+
         # mask. e.g. inference got a batch with different target durations, mask out the padding
         if mask is not None:
             attn_mask = mask
@@ -470,16 +524,36 @@ class JointAttnProcessor:
 
         batch_size = c.shape[0]
 
-        # `sample` projections.
+        # `sample` projections
         query = attn.to_q(x)
         key = attn.to_k(x)
         value = attn.to_v(x)
 
-        # `context` projections.
+        # `context` projections
         c_query = attn.to_q_c(c)
         c_key = attn.to_k_c(c)
         c_value = attn.to_v_c(c)
 
+        # attention
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        c_query = c_query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        c_key = c_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        c_value = c_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # qk norm
+        if attn.q_norm is not None:
+            query = attn.q_norm(query)
+        if attn.k_norm is not None:
+            key = attn.k_norm(key)
+        if attn.c_q_norm is not None:
+            c_query = attn.c_q_norm(c_query)
+        if attn.c_k_norm is not None:
+            c_key = attn.c_k_norm(c_key)
+
         # apply rope for context and noised input independently
         if rope is not None:
             freqs, xpos_scale = rope
@@ -492,16 +566,10 @@ class JointAttnProcessor:
             c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale)
             c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale)
 
-        # attention
-        query = torch.cat([query, c_query], dim=1)
-        key = torch.cat([key, c_key], dim=1)
-        value = torch.cat([value, c_value], dim=1)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        # joint attention
+        query = torch.cat([query, c_query], dim=2)
+        key = torch.cat([key, c_key], dim=2)
+        value = torch.cat([value, c_value], dim=2)
 
         # mask. e.g. inference got a batch with different target durations, mask out the padding
         if mask is not None:
@@ -540,16 +608,17 @@ class JointAttnProcessor:
 
 
 class DiTBlock(nn.Module):
-    def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1):
+    def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1, qk_norm=None, pe_attn_head=None):
         super().__init__()
 
-        self.attn_norm = AdaLayerNormZero(dim)
+        self.attn_norm = AdaLayerNorm(dim)
         self.attn = Attention(
-            processor=AttnProcessor(),
+            processor=AttnProcessor(pe_attn_head=pe_attn_head),
             dim=dim,
             heads=heads,
             dim_head=dim_head,
             dropout=dropout,
+            qk_norm=qk_norm,
         )
 
         self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
@@ -585,26 +654,30 @@ class MMDiTBlock(nn.Module):
     context_pre_only: last layer only do prenorm + modulation cuz no more ffn
     """
 
-    def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_pre_only=False):
+    def __init__(
+        self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_dim=None, context_pre_only=False, qk_norm=None
+    ):
         super().__init__()
-
+        if context_dim is None:
+            context_dim = dim
         self.context_pre_only = context_pre_only
 
-        self.attn_norm_c = AdaLayerNormZero_Final(dim) if context_pre_only else AdaLayerNormZero(dim)
-        self.attn_norm_x = AdaLayerNormZero(dim)
+        self.attn_norm_c = AdaLayerNorm_Final(context_dim) if context_pre_only else AdaLayerNorm(context_dim)
+        self.attn_norm_x = AdaLayerNorm(dim)
         self.attn = Attention(
             processor=JointAttnProcessor(),
             dim=dim,
             heads=heads,
             dim_head=dim_head,
             dropout=dropout,
-            context_dim=dim,
+            context_dim=context_dim,
             context_pre_only=context_pre_only,
+            qk_norm=qk_norm,
         )
 
         if not context_pre_only:
-            self.ff_norm_c = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
-            self.ff_c = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+            self.ff_norm_c = nn.LayerNorm(context_dim, elementwise_affine=False, eps=1e-6)
+            self.ff_c = FeedForward(dim=context_dim, mult=ff_mult, dropout=dropout, approximate="tanh")
         else:
             self.ff_norm_c = None
             self.ff_c = None

src/f5_tts/model/trainer.py

@@ -32,7 +32,7 @@ class Trainer:
         save_per_updates=1000,
         keep_last_n_checkpoints: int = -1,  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
         checkpoint_path=None,
-        batch_size=32,
+        batch_size_per_gpu=32,
         batch_size_type: str = "sample",
         max_samples=32,
         grad_accumulation_steps=1,
@@ -40,7 +40,7 @@ class Trainer:
         noise_scheduler: str | None = None,
         duration_predictor: torch.nn.Module | None = None,
         logger: str | None = "wandb",  # "wandb" | "tensorboard" | None
-        wandb_project="test_e2-tts",
+        wandb_project="test_f5-tts",
         wandb_run_name="test_run",
         wandb_resume_id: str = None,
         log_samples: bool = False,
@@ -51,6 +51,7 @@ class Trainer:
         mel_spec_type: str = "vocos",  # "vocos" | "bigvgan"
         is_local_vocoder: bool = False,  # use local path vocoder
         local_vocoder_path: str = "",  # local vocoder path
+        cfg_dict: dict = dict(),  # training config
     ):
         ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
 
@@ -72,21 +73,23 @@ class Trainer:
             else:
                 init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name}}
 
-            self.accelerator.init_trackers(
-                project_name=wandb_project,
-                init_kwargs=init_kwargs,
-                config={
+            if not cfg_dict:
+                cfg_dict = {
                     "epochs": epochs,
                     "learning_rate": learning_rate,
                     "num_warmup_updates": num_warmup_updates,
-                    "batch_size": batch_size,
+                    "batch_size_per_gpu": batch_size_per_gpu,
                     "batch_size_type": batch_size_type,
                     "max_samples": max_samples,
                     "grad_accumulation_steps": grad_accumulation_steps,
                     "max_grad_norm": max_grad_norm,
-                    "gpus": self.accelerator.num_processes,
                     "noise_scheduler": noise_scheduler,
-                },
+                }
+            cfg_dict["gpus"] = self.accelerator.num_processes
+            self.accelerator.init_trackers(
+                project_name=wandb_project,
+                init_kwargs=init_kwargs,
+                config=cfg_dict,
             )
 
         elif self.logger == "tensorboard":
@@ -111,9 +114,9 @@ class Trainer:
         self.save_per_updates = save_per_updates
         self.keep_last_n_checkpoints = keep_last_n_checkpoints
         self.last_per_updates = default(last_per_updates, save_per_updates)
-        self.checkpoint_path = default(checkpoint_path, "ckpts/test_e2-tts")
+        self.checkpoint_path = default(checkpoint_path, "ckpts/test_f5-tts")
 
-        self.batch_size = batch_size
+        self.batch_size_per_gpu = batch_size_per_gpu
         self.batch_size_type = batch_size_type
         self.max_samples = max_samples
         self.grad_accumulation_steps = grad_accumulation_steps
@@ -179,7 +182,7 @@ class Trainer:
         if (
             not exists(self.checkpoint_path)
             or not os.path.exists(self.checkpoint_path)
-            or not any(filename.endswith(".pt") for filename in os.listdir(self.checkpoint_path))
+            or not any(filename.endswith((".pt", ".safetensors")) for filename in os.listdir(self.checkpoint_path))
         ):
             return 0
 
@@ -191,7 +194,7 @@ class Trainer:
             all_checkpoints = [
                 f
                 for f in os.listdir(self.checkpoint_path)
-                if (f.startswith("model_") or f.startswith("pretrained_")) and f.endswith(".pt")
+                if (f.startswith("model_") or f.startswith("pretrained_")) and f.endswith((".pt", ".safetensors"))
             ]
 
             # First try to find regular training checkpoints
@@ -205,8 +208,16 @@ class Trainer:
                 # If no training checkpoints, use pretrained model
                 latest_checkpoint = next(f for f in all_checkpoints if f.startswith("pretrained_"))
 
-        # checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location=self.accelerator.device)  # rather use accelerator.load_state ಥ_ಥ
-        checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", weights_only=True, map_location="cpu")
+        if latest_checkpoint.endswith(".safetensors"):  # always a pretrained checkpoint
+            from safetensors.torch import load_file
+
+            checkpoint = load_file(f"{self.checkpoint_path}/{latest_checkpoint}", device="cpu")
+            checkpoint = {"ema_model_state_dict": checkpoint}
+        elif latest_checkpoint.endswith(".pt"):
+            # checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location=self.accelerator.device)  # rather use accelerator.load_state ಥ_ಥ
+            checkpoint = torch.load(
+                f"{self.checkpoint_path}/{latest_checkpoint}", weights_only=True, map_location="cpu"
+            )
 
         # patch for backward compatibility, 305e3ea
         for key in ["ema_model.mel_spec.mel_stft.mel_scale.fb", "ema_model.mel_spec.mel_stft.spectrogram.window"]:
@@ -271,7 +282,7 @@ class Trainer:
                 num_workers=num_workers,
                 pin_memory=True,
                 persistent_workers=True,
-                batch_size=self.batch_size,
+                batch_size=self.batch_size_per_gpu,
                 shuffle=True,
                 generator=generator,
             )
@@ -280,10 +291,10 @@ class Trainer:
             sampler = SequentialSampler(train_dataset)
             batch_sampler = DynamicBatchSampler(
                 sampler,
-                self.batch_size,
+                self.batch_size_per_gpu,
                 max_samples=self.max_samples,
                 random_seed=resumable_with_seed,  # This enables reproducible shuffling
-                drop_last=False,
+                drop_residual=False,
             )
             train_dataloader = DataLoader(
                 train_dataset,

src/f5_tts/model/utils.py

@@ -133,11 +133,12 @@ def get_tokenizer(dataset_name, tokenizer: str = "pinyin"):
 
 # convert char to pinyin
 
-jieba.initialize()
-print("Word segmentation module jieba initialized.\n")
-
 
 def convert_char_to_pinyin(text_list, polyphone=True):
+    if jieba.dt.initialized is False:
+        jieba.default_logger.setLevel(50)  # CRITICAL
+        jieba.initialize()
+
     final_text_list = []
     custom_trans = str.maketrans(
         {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}

src/f5_tts/scripts/count_max_epoch.py

@@ -9,7 +9,7 @@ mel_hop_length = 256
 mel_sampling_rate = 24000
 
 # target
-wanted_max_updates = 1000000
+wanted_max_updates = 1200000
 
 # train params
 gpus = 8

src/f5_tts/socket_client.py

@@ -0,0 +1,61 @@
+import socket
+import asyncio
+import pyaudio
+import numpy as np
+import logging
+import time
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+async def listen_to_F5TTS(text, server_ip="localhost", server_port=9998):
+    client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    await asyncio.get_event_loop().run_in_executor(None, client_socket.connect, (server_ip, int(server_port)))
+
+    start_time = time.time()
+    first_chunk_time = None
+
+    async def play_audio_stream():
+        nonlocal first_chunk_time
+        p = pyaudio.PyAudio()
+        stream = p.open(format=pyaudio.paFloat32, channels=1, rate=24000, output=True, frames_per_buffer=2048)
+
+        try:
+            while True:
+                data = await asyncio.get_event_loop().run_in_executor(None, client_socket.recv, 8192)
+                if not data:
+                    break
+                if data == b"END":
+                    logger.info("End of audio received.")
+                    break
+
+                audio_array = np.frombuffer(data, dtype=np.float32)
+                stream.write(audio_array.tobytes())
+
+                if first_chunk_time is None:
+                    first_chunk_time = time.time()
+
+        finally:
+            stream.stop_stream()
+            stream.close()
+            p.terminate()
+
+        logger.info(f"Total time taken: {time.time() - start_time:.4f} seconds")
+
+    try:
+        data_to_send = f"{text}".encode("utf-8")
+        await asyncio.get_event_loop().run_in_executor(None, client_socket.sendall, data_to_send)
+        await play_audio_stream()
+
+    except Exception as e:
+        logger.error(f"Error in listen_to_F5TTS: {e}")
+
+    finally:
+        client_socket.close()
+
+
+if __name__ == "__main__":
+    text_to_send = "As a Reader assistant, I'm familiar with new technology. which are key to its improved performance in terms of both training speed and inference efficiency. Let's break down the components"
+
+    asyncio.run(listen_to_F5TTS(text_to_send))

src/f5_tts/socket_server.py

@@ -13,8 +13,9 @@ from importlib.resources import files
 import torch
 import torchaudio
 from huggingface_hub import hf_hub_download
+from omegaconf import OmegaConf
 
-from f5_tts.model.backbones.dit import DiT
+from f5_tts.model.backbones.dit import DiT  # noqa: F401. used for config
 from f5_tts.infer.utils_infer import (
     chunk_text,
     preprocess_ref_audio_text,
@@ -68,7 +69,7 @@ class AudioFileWriterThread(threading.Thread):
 
 
 class TTSStreamingProcessor:
-    def __init__(self, ckpt_file, vocab_file, ref_audio, ref_text, device=None, dtype=torch.float32):
+    def __init__(self, model, ckpt_file, vocab_file, ref_audio, ref_text, device=None, dtype=torch.float32):
         self.device = device or (
             "cuda"
             if torch.cuda.is_available()
@@ -78,21 +79,24 @@ class TTSStreamingProcessor:
             if torch.backends.mps.is_available()
             else "cpu"
         )
-        self.mel_spec_type = "vocos"
+        model_cfg = OmegaConf.load(str(files("f5_tts").joinpath(f"configs/{model}.yaml")))
+        self.model_cls = globals()[model_cfg.model.backbone]
+        self.model_arc = model_cfg.model.arch
+        self.mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+        self.sampling_rate = model_cfg.model.mel_spec.target_sample_rate
+
         self.model = self.load_ema_model(ckpt_file, vocab_file, dtype)
         self.vocoder = self.load_vocoder_model()
-        self.sampling_rate = 24000
+
         self.update_reference(ref_audio, ref_text)
         self._warm_up()
         self.file_writer_thread = None
         self.first_package = True
 
     def load_ema_model(self, ckpt_file, vocab_file, dtype):
-        model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
-        model_cls = DiT
         return load_model(
-            model_cls=model_cls,
-            model_cfg=model_cfg,
+            self.model_cls,
+            self.model_arc,
             ckpt_path=ckpt_file,
             mel_spec_type=self.mel_spec_type,
             vocab_file=vocab_file,
@@ -212,9 +216,14 @@ if __name__ == "__main__":
     parser.add_argument("--host", default="0.0.0.0")
     parser.add_argument("--port", default=9998)
 
+    parser.add_argument(
+        "--model",
+        default="F5TTS_v1_Base",
+        help="The model name, e.g. F5TTS_v1_Base",
+    )
     parser.add_argument(
         "--ckpt_file",
-        default=str(hf_hub_download(repo_id="SWivid/F5-TTS", filename="F5TTS_Base/model_1200000.safetensors")),
+        default=str(hf_hub_download(repo_id="SWivid/F5-TTS", filename="F5TTS_v1_Base/model_1250000.safetensors")),
         help="Path to the model checkpoint file",
     )
     parser.add_argument(
@@ -242,6 +251,7 @@ if __name__ == "__main__":
     try:
         # Initialize the processor with the model and vocoder
         processor = TTSStreamingProcessor(
+            model=args.model,
             ckpt_file=args.ckpt_file,
             vocab_file=args.vocab_file,
             ref_audio=args.ref_audio,

src/f5_tts/train/README.md

@@ -40,10 +40,10 @@ Once your datasets are prepared, you can start the training process.
 accelerate config
 
 # .yaml files are under src/f5_tts/configs directory
-accelerate launch src/f5_tts/train/train.py --config-name F5TTS_Base_train.yaml
+accelerate launch src/f5_tts/train/train.py --config-name F5TTS_v1_Base_train.yaml
 
 # possible to overwrite accelerate and hydra config
-accelerate launch --mixed_precision=fp16 src/f5_tts/train/train.py --config-name F5TTS_Small_train.yaml ++datasets.batch_size_per_gpu=19200
+accelerate launch --mixed_precision=fp16 src/f5_tts/train/train.py --config-name F5TTS_v1_Base_train.yaml ++datasets.batch_size_per_gpu=19200
 ```
 
 ### 2. Finetuning practice
@@ -53,7 +53,7 @@ Gradio UI training/finetuning with `src/f5_tts/train/finetune_gradio.py` see [#1
 
 The `use_ema = True` is harmful for early-stage finetuned checkpoints (which goes just few updates, thus ema weights still dominated by pretrained ones), try turn it off and see if provide better results.
 
-### 3. Wandb Logging
+### 3. W&B Logging
 
 The `wandb/` dir will be created under path you run training/finetuning scripts.
 
@@ -62,7 +62,7 @@ By default, the training script does NOT use logging (assuming you didn't manual
 To turn on wandb logging, you can either:
 
 1. Manually login with `wandb login`: Learn more [here](https://docs.wandb.ai/ref/cli/wandb-login)
-2. Automatically login programmatically by setting an environment variable: Get an API KEY at https://wandb.ai/site/ and set the environment variable as follows:
+2. Automatically login programmatically by setting an environment variable: Get an API KEY at https://wandb.ai/authorize and set the environment variable as follows:
 
 On Mac & Linux:
 
@@ -75,7 +75,7 @@ On Windows:
 ```
 set WANDB_API_KEY=<YOUR WANDB API KEY>
 ```
-Moreover, if you couldn't access Wandb and want to log metrics offline, you can the environment variable as follows:
+Moreover, if you couldn't access W&B and want to log metrics offline, you can set the environment variable as follows:
 
 ```
 export WANDB_MODE=offline

src/f5_tts/train/finetune_cli.py

@@ -1,12 +1,13 @@
 import argparse
 import os
 import shutil
+from importlib.resources import files
 
 from cached_path import cached_path
+
 from f5_tts.model import CFM, UNetT, DiT, Trainer
 from f5_tts.model.utils import get_tokenizer
 from f5_tts.model.dataset import load_dataset
-from importlib.resources import files
 
 
 # -------------------------- Dataset Settings --------------------------- #
@@ -20,19 +21,14 @@ mel_spec_type = "vocos"  # 'vocos' or 'bigvgan'
 
 # -------------------------- Argument Parsing --------------------------- #
 def parse_args():
-    # batch_size_per_gpu = 1000 settting for gpu 8GB
-    # batch_size_per_gpu = 1600 settting for gpu 12GB
-    # batch_size_per_gpu = 2000 settting for gpu 16GB
-    # batch_size_per_gpu = 3200 settting for gpu 24GB
-
-    # num_warmup_updates = 300 for 5000 sample about 10 hours
-
-    # change save_per_updates , last_per_updates change this value what you need  ,
-
     parser = argparse.ArgumentParser(description="Train CFM Model")
 
     parser.add_argument(
-        "--exp_name", type=str, default="F5TTS_Base", choices=["F5TTS_Base", "E2TTS_Base"], help="Experiment name"
+        "--exp_name",
+        type=str,
+        default="F5TTS_v1_Base",
+        choices=["F5TTS_v1_Base", "F5TTS_Base", "E2TTS_Base"],
+        help="Experiment name",
     )
     parser.add_argument("--dataset_name", type=str, default="Emilia_ZH_EN", help="Name of the dataset to use")
     parser.add_argument("--learning_rate", type=float, default=1e-5, help="Learning rate for training")
@@ -88,19 +84,54 @@ def main():
     checkpoint_path = str(files("f5_tts").joinpath(f"../../ckpts/{args.dataset_name}"))
 
     # Model parameters based on experiment name
-    if args.exp_name == "F5TTS_Base":
+
+    if args.exp_name == "F5TTS_v1_Base":
         wandb_resume_id = None
         model_cls = DiT
-        model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+        model_cfg = dict(
+            dim=1024,
+            depth=22,
+            heads=16,
+            ff_mult=2,
+            text_dim=512,
+            conv_layers=4,
+        )
+        if args.finetune:
+            if args.pretrain is None:
+                ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_v1_Base/model_1250000.safetensors"))
+            else:
+                ckpt_path = args.pretrain
+
+    elif args.exp_name == "F5TTS_Base":
+        wandb_resume_id = None
+        model_cls = DiT
+        model_cfg = dict(
+            dim=1024,
+            depth=22,
+            heads=16,
+            ff_mult=2,
+            text_dim=512,
+            text_mask_padding=False,
+            conv_layers=4,
+            pe_attn_head=1,
+        )
         if args.finetune:
             if args.pretrain is None:
                 ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
             else:
                 ckpt_path = args.pretrain
+
     elif args.exp_name == "E2TTS_Base":
         wandb_resume_id = None
         model_cls = UNetT
-        model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+        model_cfg = dict(
+            dim=1024,
+            depth=24,
+            heads=16,
+            ff_mult=4,
+            text_mask_padding=False,
+            pe_attn_head=1,
+        )
         if args.finetune:
             if args.pretrain is None:
                 ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
@@ -120,6 +151,7 @@ def main():
             print("copy checkpoint for finetune")
 
     # Use the tokenizer and tokenizer_path provided in the command line arguments
+
     tokenizer = args.tokenizer
     if tokenizer == "custom":
         if not args.tokenizer_path:
@@ -156,7 +188,7 @@ def main():
         save_per_updates=args.save_per_updates,
         keep_last_n_checkpoints=args.keep_last_n_checkpoints,
         checkpoint_path=checkpoint_path,
-        batch_size=args.batch_size_per_gpu,
+        batch_size_per_gpu=args.batch_size_per_gpu,
         batch_size_type=args.batch_size_type,
         max_samples=args.max_samples,
         grad_accumulation_steps=args.grad_accumulation_steps,

src/f5_tts/train/finetune_gradio.py

@@ -1,36 +1,36 @@
-import threading
-import queue
-import re
-
 import gc
 import json
+import numpy as np
 import os
 import platform
 import psutil
+import queue
 import random
+import re
 import signal
 import shutil
 import subprocess
 import sys
 import tempfile
+import threading
 import time
 from glob import glob
+from importlib.resources import files
+from scipy.io import wavfile
 
 import click
 import gradio as gr
 import librosa
-import numpy as np
 import torch
 import torchaudio
+from cached_path import cached_path
 from datasets import Dataset as Dataset_
 from datasets.arrow_writer import ArrowWriter
-from safetensors.torch import save_file
-from scipy.io import wavfile
-from cached_path import cached_path
+from safetensors.torch import load_file, save_file
+
 from f5_tts.api import F5TTS
 from f5_tts.model.utils import convert_char_to_pinyin
 from f5_tts.infer.utils_infer import transcribe
-from importlib.resources import files
 
 
 training_process = None
@@ -118,16 +118,16 @@ def load_settings(project_name):
 
     # Default settings
     default_settings = {
-        "exp_name": "F5TTS_Base",
-        "learning_rate": 1e-05,
-        "batch_size_per_gpu": 1000,
-        "batch_size_type": "frame",
+        "exp_name": "F5TTS_v1_Base",
+        "learning_rate": 1e-5,
+        "batch_size_per_gpu": 1,
+        "batch_size_type": "sample",
         "max_samples": 64,
-        "grad_accumulation_steps": 1,
+        "grad_accumulation_steps": 4,
         "max_grad_norm": 1,
         "epochs": 100,
-        "num_warmup_updates": 2,
-        "save_per_updates": 300,
+        "num_warmup_updates": 100,
+        "save_per_updates": 500,
         "keep_last_n_checkpoints": -1,
         "last_per_updates": 100,
         "finetune": True,
@@ -362,18 +362,18 @@ def terminate_process(pid):
 
 def start_training(
     dataset_name="",
-    exp_name="F5TTS_Base",
-    learning_rate=1e-4,
-    batch_size_per_gpu=400,
-    batch_size_type="frame",
+    exp_name="F5TTS_v1_Base",
+    learning_rate=1e-5,
+    batch_size_per_gpu=1,
+    batch_size_type="sample",
     max_samples=64,
-    grad_accumulation_steps=1,
+    grad_accumulation_steps=4,
     max_grad_norm=1.0,
-    epochs=11,
-    num_warmup_updates=200,
-    save_per_updates=400,
+    epochs=100,
+    num_warmup_updates=100,
+    save_per_updates=500,
     keep_last_n_checkpoints=-1,
-    last_per_updates=800,
+    last_per_updates=100,
     finetune=True,
     file_checkpoint_train="",
     tokenizer_type="pinyin",
@@ -797,14 +797,14 @@ def create_metadata(name_project, ch_tokenizer, progress=gr.Progress()):
             print(f"Error processing {file_audio}: {e}")
             continue
 
-        if duration < 1 or duration > 25:
-            if duration > 25:
-                error_files.append([file_audio, "duration > 25 sec"])
+        if duration < 1 or duration > 30:
+            if duration > 30:
+                error_files.append([file_audio, "duration > 30 sec"])
             if duration < 1:
                 error_files.append([file_audio, "duration < 1 sec "])
             continue
         if len(text) < 3:
-            error_files.append([file_audio, "very small text len 3"])
+            error_files.append([file_audio, "very short text length 3"])
             continue
 
         text = clear_text(text)
@@ -871,40 +871,37 @@ def check_user(value):
 
 def calculate_train(
     name_project,
+    epochs,
+    learning_rate,
+    batch_size_per_gpu,
     batch_size_type,
     max_samples,
-    learning_rate,
     num_warmup_updates,
-    save_per_updates,
-    last_per_updates,
     finetune,
 ):
     path_project = os.path.join(path_data, name_project)
-    file_duraction = os.path.join(path_project, "duration.json")
+    file_duration = os.path.join(path_project, "duration.json")
+
+    hop_length = 256
+    sampling_rate = 24000
 
-    if not os.path.isfile(file_duraction):
+    if not os.path.isfile(file_duration):
         return (
-            1000,
+            epochs,
+            learning_rate,
+            batch_size_per_gpu,
             max_samples,
             num_warmup_updates,
-            save_per_updates,
-            last_per_updates,
             "project not found !",
-            learning_rate,
         )
 
-    with open(file_duraction, "r") as file:
+    with open(file_duration, "r") as file:
         data = json.load(file)
 
     duration_list = data["duration"]
-    samples = len(duration_list)
-    hours = sum(duration_list) / 3600
-
-    # if torch.cuda.is_available():
-    # gpu_properties = torch.cuda.get_device_properties(0)
-    # total_memory = gpu_properties.total_memory / (1024**3)
-    # elif torch.backends.mps.is_available():
-    # total_memory = psutil.virtual_memory().available / (1024**3)
+    max_sample_length = max(duration_list) * sampling_rate / hop_length
+    total_samples = len(duration_list)
+    total_duration = sum(duration_list)
 
     if torch.cuda.is_available():
         gpu_count = torch.cuda.device_count()
@@ -912,64 +909,39 @@ def calculate_train(
         for i in range(gpu_count):
             gpu_properties = torch.cuda.get_device_properties(i)
             total_memory += gpu_properties.total_memory / (1024**3)  # in GB
-
     elif torch.xpu.is_available():
         gpu_count = torch.xpu.device_count()
         total_memory = 0
         for i in range(gpu_count):
             gpu_properties = torch.xpu.get_device_properties(i)
             total_memory += gpu_properties.total_memory / (1024**3)
-
     elif torch.backends.mps.is_available():
         gpu_count = 1
         total_memory = psutil.virtual_memory().available / (1024**3)
 
+    avg_gpu_memory = total_memory / gpu_count
+
+    # rough estimate of batch size
     if batch_size_type == "frame":
-        batch = int(total_memory * 0.5)
-        batch = (lambda num: num + 1 if num % 2 != 0 else num)(batch)
-        batch_size_per_gpu = int(38400 / batch)
-    else:
-        batch_size_per_gpu = int(total_memory / 8)
-        batch_size_per_gpu = (lambda num: num + 1 if num % 2 != 0 else num)(batch_size_per_gpu)
-        batch = batch_size_per_gpu
+        batch_size_per_gpu = max(int(38400 * (avg_gpu_memory - 5) / 75), int(max_sample_length))
+    elif batch_size_type == "sample":
+        batch_size_per_gpu = int(200 / (total_duration / total_samples))
 
-    if batch_size_per_gpu <= 0:
-        batch_size_per_gpu = 1
+    if total_samples < 64:
+        max_samples = int(total_samples * 0.25)
 
-    if samples < 64:
-        max_samples = int(samples * 0.25)
-    else:
-        max_samples = 64
-
-    num_warmup_updates = int(samples * 0.05)
-    save_per_updates = int(samples * 0.10)
-    last_per_updates = int(save_per_updates * 0.25)
-
-    max_samples = (lambda num: num + 1 if num % 2 != 0 else num)(max_samples)
-    num_warmup_updates = (lambda num: num + 1 if num % 2 != 0 else num)(num_warmup_updates)
-    save_per_updates = (lambda num: num + 1 if num % 2 != 0 else num)(save_per_updates)
-    last_per_updates = (lambda num: num + 1 if num % 2 != 0 else num)(last_per_updates)
-    if last_per_updates <= 0:
-        last_per_updates = 2
-
-    total_hours = hours
-    mel_hop_length = 256
-    mel_sampling_rate = 24000
-
-    # target
-    wanted_max_updates = 1000000
-
-    # train params
-    gpus = gpu_count
-    frames_per_gpu = batch_size_per_gpu  # 8 * 38400 = 307200
-    grad_accum = 1
-
-    # intermediate
-    mini_batch_frames = frames_per_gpu * grad_accum * gpus
-    mini_batch_hours = mini_batch_frames * mel_hop_length / mel_sampling_rate / 3600
-    updates_per_epoch = total_hours / mini_batch_hours
-    # steps_per_epoch = updates_per_epoch * grad_accum
-    epochs = wanted_max_updates / updates_per_epoch
+    num_warmup_updates = max(num_warmup_updates, int(total_samples * 0.05))
+
+    # take 1.2M updates as the maximum
+    max_updates = 1200000
+
+    if batch_size_type == "frame":
+        mini_batch_duration = batch_size_per_gpu * gpu_count * hop_length / sampling_rate
+        updates_per_epoch = total_duration / mini_batch_duration
+    elif batch_size_type == "sample":
+        updates_per_epoch = total_samples / batch_size_per_gpu / gpu_count
+
+    epochs = int(max_updates / updates_per_epoch)
 
     if finetune:
         learning_rate = 1e-5
@@ -977,14 +949,12 @@ def calculate_train(
         learning_rate = 7.5e-5
 
     return (
+        epochs,
+        learning_rate,
         batch_size_per_gpu,
         max_samples,
         num_warmup_updates,
-        save_per_updates,
-        last_per_updates,
-        samples,
-        learning_rate,
-        int(epochs),
+        total_samples,
     )
 
 
@@ -1021,7 +991,11 @@ def expand_model_embeddings(ckpt_path, new_ckpt_path, num_new_tokens=42):
     torch.backends.cudnn.deterministic = True
     torch.backends.cudnn.benchmark = False
 
-    ckpt = torch.load(ckpt_path, map_location="cpu")
+    if ckpt_path.endswith(".safetensors"):
+        ckpt = load_file(ckpt_path, device="cpu")
+        ckpt = {"ema_model_state_dict": ckpt}
+    elif ckpt_path.endswith(".pt"):
+        ckpt = torch.load(ckpt_path, map_location="cpu")
 
     ema_sd = ckpt.get("ema_model_state_dict", {})
     embed_key_ema = "ema_model.transformer.text_embed.text_embed.weight"
@@ -1089,9 +1063,11 @@ def vocab_extend(project_name, symbols, model_type):
     with open(file_vocab_project, "w", encoding="utf-8") as f:
         f.write("\n".join(vocab))
 
-    if model_type == "F5-TTS":
+    if model_type == "F5TTS_v1_Base":
+        ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_v1_Base/model_1250000.safetensors"))
+    elif model_type == "F5TTS_Base":
         ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
-    else:
+    elif model_type == "E2TTS_Base":
         ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
 
     vocab_size_new = len(miss_symbols)
@@ -1101,7 +1077,7 @@ def vocab_extend(project_name, symbols, model_type):
     os.makedirs(new_ckpt_path, exist_ok=True)
 
     # Add pretrained_ prefix to model when copying for consistency with finetune_cli.py
-    new_ckpt_file = os.path.join(new_ckpt_path, "pretrained_model_1200000.pt")
+    new_ckpt_file = os.path.join(new_ckpt_path, "pretrained_" + os.path.basename(ckpt_path))
 
     size = expand_model_embeddings(ckpt_path, new_ckpt_file, num_new_tokens=vocab_size_new)
 
@@ -1231,21 +1207,21 @@ def infer(
         vocab_file = os.path.join(path_data, project, "vocab.txt")
 
         tts_api = F5TTS(
-            model_type=exp_name, ckpt_file=file_checkpoint, vocab_file=vocab_file, device=device_test, use_ema=use_ema
+            model=exp_name, ckpt_file=file_checkpoint, vocab_file=vocab_file, device=device_test, use_ema=use_ema
         )
 
         print("update >> ", device_test, file_checkpoint, use_ema)
 
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
         tts_api.infer(
-            gen_text=gen_text.lower().strip(),
-            ref_text=ref_text.lower().strip(),
             ref_file=ref_audio,
+            ref_text=ref_text.lower().strip(),
+            gen_text=gen_text.lower().strip(),
             nfe_step=nfe_step,
-            file_wave=f.name,
             speed=speed,
-            seed=seed,
             remove_silence=remove_silence,
+            file_wave=f.name,
+            seed=seed,
         )
         return f.name, tts_api.device, str(tts_api.seed)
 
@@ -1404,14 +1380,14 @@ def get_audio_select(file_sample):
 with gr.Blocks() as app:
     gr.Markdown(
         """
-# E2/F5 TTS Automatic Finetune
+# F5 TTS Automatic Finetune
 
-This is a local web UI for F5 TTS with advanced batch processing support. This app supports the following TTS models:
+This is a local web UI for F5 TTS finetuning support. This app supports the following TTS models:
 
 * [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching)
 * [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS)
 
-The checkpoints support English and Chinese.
+The pretrained checkpoints support English and Chinese.
 
 For tutorial and updates check here (https://github.com/SWivid/F5-TTS/discussions/143)
 """
@@ -1488,7 +1464,9 @@ Check the vocabulary for fine-tuning Emilia_ZH_EN to ensure all symbols are incl
 Using the extended model, you can finetune to a new language that is missing symbols in the vocab. This creates a new model with a new vocabulary size and saves it in your ckpts/project folder.
 ```""")
 
-            exp_name_extend = gr.Radio(label="Model", choices=["F5-TTS", "E2-TTS"], value="F5-TTS")
+            exp_name_extend = gr.Radio(
+                label="Model", choices=["F5TTS_v1_Base", "F5TTS_Base", "E2TTS_Base"], value="F5TTS_v1_Base"
+            )
 
             with gr.Row():
                 txt_extend = gr.Textbox(
@@ -1557,9 +1535,9 @@ Skip this step if you have your dataset, raw.arrow, duration.json, and vocab.txt
                 fn=get_random_sample_prepare, inputs=[cm_project], outputs=[random_text_prepare, random_audio_prepare]
             )
 
-        with gr.TabItem("Train Data"):
+        with gr.TabItem("Train Model"):
             gr.Markdown("""```plaintext 
-The auto-setting is still experimental. Please make sure that the epochs, save per updates, and last per updates are set correctly, or change them manually as needed.
+The auto-setting is still experimental. Set a large value of epoch if not sure; and keep last N checkpoints if limited disk space.
 If you encounter a memory error, try reducing the batch size per GPU to a smaller number.
 ```""")
             with gr.Row():
@@ -1573,11 +1551,13 @@ If you encounter a memory error, try reducing the batch size per GPU to a smalle
                 file_checkpoint_train = gr.Textbox(label="Path to the Pretrained Checkpoint", value="")
 
             with gr.Row():
-                exp_name = gr.Radio(label="Model", choices=["F5TTS_Base", "E2TTS_Base"], value="F5TTS_Base")
+                exp_name = gr.Radio(
+                    label="Model", choices=["F5TTS_v1_Base", "F5TTS_Base", "E2TTS_Base"], value="F5TTS_v1_Base"
+                )
                 learning_rate = gr.Number(label="Learning Rate", value=1e-5, step=1e-5)
 
             with gr.Row():
-                batch_size_per_gpu = gr.Number(label="Batch Size per GPU", value=1000)
+                batch_size_per_gpu = gr.Number(label="Batch Size per GPU", value=3200)
                 max_samples = gr.Number(label="Max Samples", value=64)
 
             with gr.Row():
@@ -1585,23 +1565,23 @@ If you encounter a memory error, try reducing the batch size per GPU to a smalle
                 max_grad_norm = gr.Number(label="Max Gradient Norm", value=1.0)
 
             with gr.Row():
-                epochs = gr.Number(label="Epochs", value=10)
-                num_warmup_updates = gr.Number(label="Warmup Updates", value=2)
+                epochs = gr.Number(label="Epochs", value=100)
+                num_warmup_updates = gr.Number(label="Warmup Updates", value=100)
 
             with gr.Row():
-                save_per_updates = gr.Number(label="Save per Updates", value=300)
+                save_per_updates = gr.Number(label="Save per Updates", value=500)
                 keep_last_n_checkpoints = gr.Number(
                     label="Keep Last N Checkpoints",
                     value=-1,
                     step=1,
                     precision=0,
-                    info="-1: Keep all checkpoints, 0: Only save final model_last.pt, N>0: Keep last N checkpoints",
+                    info="-1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints",
                 )
                 last_per_updates = gr.Number(label="Last per Updates", value=100)
 
             with gr.Row():
                 ch_8bit_adam = gr.Checkbox(label="Use 8-bit Adam optimizer")
-                mixed_precision = gr.Radio(label="mixed_precision", choices=["none", "fp16", "bf16"], value="none")
+                mixed_precision = gr.Radio(label="mixed_precision", choices=["none", "fp16", "bf16"], value="fp16")
                 cd_logger = gr.Radio(label="logger", choices=["wandb", "tensorboard"], value="wandb")
                 start_button = gr.Button("Start Training")
                 stop_button = gr.Button("Stop Training", interactive=False)
@@ -1718,23 +1698,21 @@ If you encounter a memory error, try reducing the batch size per GPU to a smalle
                 fn=calculate_train,
                 inputs=[
                     cm_project,
+                    epochs,
+                    learning_rate,
+                    batch_size_per_gpu,
                     batch_size_type,
                     max_samples,
-                    learning_rate,
                     num_warmup_updates,
-                    save_per_updates,
-                    last_per_updates,
                     ch_finetune,
                 ],
                 outputs=[
+                    epochs,
+                    learning_rate,
                     batch_size_per_gpu,
                     max_samples,
                     num_warmup_updates,
-                    save_per_updates,
-                    last_per_updates,
                     lb_samples,
-                    learning_rate,
-                    epochs,
                 ],
             )
 
@@ -1744,25 +1722,25 @@ If you encounter a memory error, try reducing the batch size per GPU to a smalle
 
             def setup_load_settings():
                 output_components = [
-                    exp_name,  # 1
-                    learning_rate,  # 2
-                    batch_size_per_gpu,  # 3
-                    batch_size_type,  # 4
-                    max_samples,  # 5
-                    grad_accumulation_steps,  # 6
-                    max_grad_norm,  # 7
-                    epochs,  # 8
-                    num_warmup_updates,  # 9
-                    save_per_updates,  # 10
-                    keep_last_n_checkpoints,  # 11
-                    last_per_updates,  # 12
-                    ch_finetune,  # 13
-                    file_checkpoint_train,  # 14
-                    tokenizer_type,  # 15
-                    tokenizer_file,  # 16
-                    mixed_precision,  # 17
-                    cd_logger,  # 18
-                    ch_8bit_adam,  # 19
+                    exp_name,
+                    learning_rate,
+                    batch_size_per_gpu,
+                    batch_size_type,
+                    max_samples,
+                    grad_accumulation_steps,
+                    max_grad_norm,
+                    epochs,
+                    num_warmup_updates,
+                    save_per_updates,
+                    keep_last_n_checkpoints,
+                    last_per_updates,
+                    ch_finetune,
+                    file_checkpoint_train,
+                    tokenizer_type,
+                    tokenizer_file,
+                    mixed_precision,
+                    cd_logger,
+                    ch_8bit_adam,
                 ]
                 return output_components
 
@@ -1784,7 +1762,9 @@ If you encounter a memory error, try reducing the batch size per GPU to a smalle
             gr.Markdown("""```plaintext 
 SOS: Check the use_ema setting (True or False) for your model to see what works best for you. use seed -1 from random
 ```""")
-            exp_name = gr.Radio(label="Model", choices=["F5-TTS", "E2-TTS"], value="F5-TTS")
+            exp_name = gr.Radio(
+                label="Model", choices=["F5TTS_v1_Base", "F5TTS_Base", "E2TTS_Base"], value="F5TTS_v1_Base"
+            )
             list_checkpoints, checkpoint_select = get_checkpoints_project(projects_selelect, False)
 
             with gr.Row():
@@ -1838,9 +1818,9 @@ SOS: Check the use_ema setting (True or False) for your model to see what works
             bt_checkpoint_refresh.click(fn=get_checkpoints_project, inputs=[cm_project], outputs=[cm_checkpoint])
             cm_project.change(fn=get_checkpoints_project, inputs=[cm_project], outputs=[cm_checkpoint])
 
-        with gr.TabItem("Reduce Checkpoint"):
+        with gr.TabItem("Prune Checkpoint"):
             gr.Markdown("""```plaintext 
-Reduce the model size from 5GB to 1.3GB. The new checkpoint can be used for inference or fine-tuning afterward, but it cannot be used to continue training.
+Reduce the Base model size from 5GB to 1.3GB. The new checkpoint file prunes out optimizer and etc., can be used for inference or finetuning afterward, but not able to resume pretraining.
 ```""")
             txt_path_checkpoint = gr.Text(label="Path to Checkpoint:")
             txt_path_checkpoint_small = gr.Text(label="Path to Output:")

src/f5_tts/train/train.py

@@ -4,8 +4,9 @@ import os
 from importlib.resources import files
 
 import hydra
+from omegaconf import OmegaConf
 
-from f5_tts.model import CFM, DiT, Trainer, UNetT
+from f5_tts.model import CFM, DiT, UNetT, Trainer  # noqa: F401. used for config
 from f5_tts.model.dataset import load_dataset
 from f5_tts.model.utils import get_tokenizer
 
@@ -14,9 +15,13 @@ os.chdir(str(files("f5_tts").joinpath("../..")))  # change working directory to
 
 @hydra.main(version_base="1.3", config_path=str(files("f5_tts").joinpath("configs")), config_name=None)
 def main(cfg):
+    model_cls = globals()[cfg.model.backbone]
+    model_arc = cfg.model.arch
     tokenizer = cfg.model.tokenizer
     mel_spec_type = cfg.model.mel_spec.mel_spec_type
+
     exp_name = f"{cfg.model.name}_{mel_spec_type}_{cfg.model.tokenizer}_{cfg.datasets.name}"
+    wandb_resume_id = None
 
     # set text tokenizer
     if tokenizer != "custom":
@@ -26,14 +31,8 @@ def main(cfg):
     vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer)
 
     # set model
-    if "F5TTS" in cfg.model.name:
-        model_cls = DiT
-    elif "E2TTS" in cfg.model.name:
-        model_cls = UNetT
-    wandb_resume_id = None
-
     model = CFM(
-        transformer=model_cls(**cfg.model.arch, text_num_embeds=vocab_size, mel_dim=cfg.model.mel_spec.n_mel_channels),
+        transformer=model_cls(**model_arc, text_num_embeds=vocab_size, mel_dim=cfg.model.mel_spec.n_mel_channels),
         mel_spec_kwargs=cfg.model.mel_spec,
         vocab_char_map=vocab_char_map,
     )
@@ -45,9 +44,9 @@ def main(cfg):
         learning_rate=cfg.optim.learning_rate,
         num_warmup_updates=cfg.optim.num_warmup_updates,
         save_per_updates=cfg.ckpts.save_per_updates,
-        keep_last_n_checkpoints=getattr(cfg.ckpts, "keep_last_n_checkpoints", -1),
+        keep_last_n_checkpoints=cfg.ckpts.keep_last_n_checkpoints,
         checkpoint_path=str(files("f5_tts").joinpath(f"../../{cfg.ckpts.save_dir}")),
-        batch_size=cfg.datasets.batch_size_per_gpu,
+        batch_size_per_gpu=cfg.datasets.batch_size_per_gpu,
         batch_size_type=cfg.datasets.batch_size_type,
         max_samples=cfg.datasets.max_samples,
         grad_accumulation_steps=cfg.optim.grad_accumulation_steps,
@@ -57,11 +56,12 @@ def main(cfg):
         wandb_run_name=exp_name,
         wandb_resume_id=wandb_resume_id,
         last_per_updates=cfg.ckpts.last_per_updates,
-        log_samples=True,
+        log_samples=cfg.ckpts.log_samples,
         bnb_optimizer=cfg.optim.bnb_optimizer,
         mel_spec_type=mel_spec_type,
         is_local_vocoder=cfg.model.vocoder.is_local,
         local_vocoder_path=cfg.model.vocoder.local_path,
+        cfg_dict=OmegaConf.to_container(cfg, resolve=True),
     )
 
     train_dataset = load_dataset(cfg.datasets.name, tokenizer, mel_spec_kwargs=cfg.model.mel_spec)