reorganize infer_cli and stuff

README.md

@@ -147,11 +147,11 @@ Note: Some model components have linting exceptions for E722 to accommodate tens
 ## Acknowledgements
 
 - [E2-TTS](https://arxiv.org/abs/2406.18009) brilliant work, simple and effective
-- [Emilia](https://arxiv.org/abs/2407.05361), [WenetSpeech4TTS](https://arxiv.org/abs/2406.05763) valuable datasets
+- [Emilia](https://arxiv.org/abs/2407.05361), [WenetSpeech4TTS](https://arxiv.org/abs/2406.05763), [LibriTTS](https://arxiv.org/abs/1904.02882), [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) valuable datasets
 - [lucidrains](https://github.com/lucidrains) initial CFM structure with also [bfs18](https://github.com/bfs18) for discussion
 - [SD3](https://arxiv.org/abs/2403.03206) & [Hugging Face diffusers](https://github.com/huggingface/diffusers) DiT and MMDiT code structure
 - [torchdiffeq](https://github.com/rtqichen/torchdiffeq) as ODE solver, [Vocos](https://huggingface.co/charactr/vocos-mel-24khz) as vocoder
-- [FunASR](https://github.com/modelscope/FunASR), [faster-whisper](https://github.com/SYSTRAN/faster-whisper), [UniSpeech](https://github.com/microsoft/UniSpeech) for evaluation tools
+- [FunASR](https://github.com/modelscope/FunASR), [faster-whisper](https://github.com/SYSTRAN/faster-whisper), [UniSpeech](https://github.com/microsoft/UniSpeech), [SpeechMOS](https://github.com/tarepan/SpeechMOS) for evaluation tools
 - [ctc-forced-aligner](https://github.com/MahmoudAshraf97/ctc-forced-aligner) for speech edit test
 - [mrfakename](https://x.com/realmrfakename) huggingface space demo ~
 - [f5-tts-mlx](https://github.com/lucasnewman/f5-tts-mlx/tree/main) Implementation with MLX framework by [Lucas Newman](https://github.com/lucasnewman)

src/f5_tts/eval/README.md

@@ -39,11 +39,14 @@ Then update in the following scripts with the paths you put evaluation model ckp
 
 ### Objective Evaluation
 
-Update the path with your batch-inferenced results, and carry out WER / SIM evaluations:
+Update the path with your batch-inferenced results, and carry out WER / SIM / UTMOS evaluations:
 ```bash
-# Evaluation for Seed-TTS test set
-python src/f5_tts/eval/eval_seedtts_testset.py --gen_wav_dir <GEN_WAVE_DIR>
+# Evaluation [WER] for Seed-TTS test [ZH] set
+python src/f5_tts/eval/eval_seedtts_testset.py --eval_task wer --lang zh --gen_wav_dir <GEN_WAV_DIR> --gpu_nums 8
 
-# Evaluation for LibriSpeech-PC test-clean (cross-sentence)
-python src/f5_tts/eval/eval_librispeech_test_clean.py --gen_wav_dir <GEN_WAVE_DIR> --librispeech_test_clean_path <TEST_CLEAN_PATH>
-```
+# Evaluation [SIM] for LibriSpeech-PC test-clean (cross-sentence)
+python src/f5_tts/eval/eval_librispeech_test_clean.py --eval_task sim --gen_wav_dir <GEN_WAV_DIR> --librispeech_test_clean_path <TEST_CLEAN_PATH>
+
+# Evaluation [UTMOS]. --ext: Audio extension
+python src/f5_tts/eval/eval_utmos.py --audio_dir <WAV_DIR> --ext wav
+```

src/f5_tts/eval/eval_librispeech_test_clean.py

@@ -1,8 +1,9 @@
 # Evaluate with Librispeech test-clean, ~3s prompt to generate 4-10s audio (the way of valle/voicebox evaluation)
 
-import sys
-import os
 import argparse
+import json
+import os
+import sys
 
 sys.path.append(os.getcwd())
 
@@ -10,7 +11,6 @@ import multiprocessing as mp
 from importlib.resources import files
 
 import numpy as np
-import json
 from f5_tts.eval.utils_eval import (
     get_librispeech_test,
     run_asr_wer,
@@ -54,36 +54,41 @@ def main():
     wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth"
 
     # --------------------------- WER ---------------------------
+
     if eval_task == "wer":
-        wers = []
         wer_results = []
+        wers = []
+
         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 wers_ in results:
-                wers.extend(wers_)
+            for r in results:
+                wer_results.extend(r)
 
-        with open(f"{gen_wav_dir}/{lang}_wer_results.jsonl", "w") as f:
-            for line in wers:
-                wer_results.append(line["wer"])
+        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":
-        sim_list = []
+        sims = []
         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 sim_ in results:
-                sim_list.extend(sim_)
+            for r in results:
+                sims.extend(r)
 
-        sim = round(sum(sim_list) / len(sim_list), 3)
-        print(f"\nTotal {len(sim_list)} samples")
+        sim = round(sum(sims) / len(sims), 3)
+        print(f"\nTotal {len(sims)} samples")
         print(f"SIM      : {sim}")
 
 

src/f5_tts/eval/eval_seedtts_testset.py

@@ -1,8 +1,9 @@
 # Evaluate with Seed-TTS testset
 
-import sys
-import os
 import argparse
+import json
+import os
+import sys
 
 sys.path.append(os.getcwd())
 
@@ -10,7 +11,6 @@ import multiprocessing as mp
 from importlib.resources import files
 
 import numpy as np
-import json
 from f5_tts.eval.utils_eval import (
     get_seed_tts_test,
     run_asr_wer,
@@ -55,35 +55,39 @@ def main():
     # --------------------------- WER ---------------------------
 
     if eval_task == "wer":
-        wers = []
         wer_results = []
+        wers = []
+
         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 wers_ in results:
-                wers.extend(wers_)
+            for r in results:
+                wer_results.extend(r)
 
-        with open(f"{gen_wav_dir}/{lang}_wer_results.jsonl", "w") as f:
-            for line in wers:
-                wer_results.append(line["wer"])
+        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":
-        sim_list = []
+        sims = []
         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 sim_ in results:
-                sim_list.extend(sim_)
+            for r in results:
+                sims.extend(r)
 
-        sim = round(sum(sim_list) / len(sim_list), 3)
-        print(f"\nTotal {len(sim_list)} samples")
+        sim = round(sum(sims) / len(sims), 3)
+        print(f"\nTotal {len(sims)} samples")
         print(f"SIM      : {sim}")
 
 

src/f5_tts/eval/eval_utmos.py

@@ -1,46 +1,43 @@
-import torch
-import librosa
-from pathlib import Path
+import argparse
 import json
+from pathlib import Path
+
+import librosa
+import torch
 from tqdm import tqdm
-import argparse
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Evaluate UTMOS scores for audio files.")
-    parser.add_argument(
-        "--audio_dir", type=str, required=True, help="Path to the directory containing WAV audio files."
-    )
-    parser.add_argument("--ext", type=str, default="wav", help="audio extension.")
-    parser.add_argument("--device", type=str, default="cuda", help="Device to run inference on (e.g. 'cuda' or 'cpu').")
-
+    parser = argparse.ArgumentParser(description="UTMOS Evaluation")
+    parser.add_argument("--audio_dir", type=str, required=True, help="Audio file path.")
+    parser.add_argument("--ext", type=str, default="wav", help="Audio extension.")
     args = parser.parse_args()
 
-    device = "cuda" if args.device and torch.cuda.is_available() else "cpu"
+    device = "cuda" if torch.cuda.is_available() else "cpu"
 
     predictor = torch.hub.load("tarepan/SpeechMOS:v1.2.0", "utmos22_strong", trust_repo=True)
     predictor = predictor.to(device)
 
-    lines = list(Path(args.audio_dir).rglob(f"*.{args.ext}"))
-    results = {}
-    utmos_result = 0
+    audio_paths = list(Path(args.audio_dir).rglob(f"*.{args.ext}"))
+    utmos_results = {}
+    utmos_score = 0
 
-    for line in tqdm(lines, desc="Processing"):
-        wave_name = line.stem
-        wave, sr = librosa.load(line, sr=None, mono=True)
-        wave_tensor = torch.from_numpy(wave).to(device).unsqueeze(0)
-        score = predictor(wave_tensor, sr)
-        results[str(wave_name)] = score.item()
-        utmos_result += score.item()
+    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_result / len(lines) if len(lines) > 0 else 0
+    avg_score = utmos_score / len(audio_paths) if len(audio_paths) > 0 else 0
     print(f"UTMOS: {avg_score}")
 
-    output_path = Path(args.audio_dir) / "utmos_results.json"
-    with open(output_path, "w", encoding="utf-8") as f:
-        json.dump(results, f, ensure_ascii=False, indent=4)
+    utmos_result_path = Path(args.audio_dir) / "utmos_results.json"
+    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 {output_path}")
+    print(f"Results have been saved to {utmos_result_path}")
 
 
 if __name__ == "__main__":

src/f5_tts/eval/utils_eval.py

@@ -2,12 +2,13 @@ import math
 import os
 import random
 import string
+from pathlib import Path
 
 import torch
 import torch.nn.functional as F
 import torchaudio
 from tqdm import tqdm
-from pathlib import Path
+
 from f5_tts.eval.ecapa_tdnn import ECAPA_TDNN_SMALL
 from f5_tts.model.modules import MelSpec
 from f5_tts.model.utils import convert_char_to_pinyin
@@ -320,7 +321,7 @@ def run_asr_wer(args):
     from zhon.hanzi import punctuation
 
     punctuation_all = punctuation + string.punctuation
-    wers = []
+    wer_results = []
 
     from jiwer import compute_measures
 
@@ -335,8 +336,8 @@ def run_asr_wer(args):
             for segment in segments:
                 hypo = hypo + " " + segment.text
 
-        # raw_truth = truth
-        # raw_hypo = hypo
+        raw_truth = truth
+        raw_hypo = hypo
 
         for x in punctuation_all:
             truth = truth.replace(x, "")
@@ -360,16 +361,16 @@ def run_asr_wer(args):
         # dele = measures["deletions"] / len(ref_list)
         # inse = measures["insertions"] / len(ref_list)
 
-        wers.append(
+        wer_results.append(
             {
-                "wav": Path(gen_wav).stem,  # wav name
-                "truth": truth,  # raw_truth
-                "hypo": hypo,  # raw_hypo
-                "wer": wer,  # wer score
+                "wav": Path(gen_wav).stem,
+                "truth": raw_truth,
+                "hypo": raw_hypo,
+                "wer": wer,
             }
         )
 
-    return wers
+    return wer_results
 
 
 # SIM Evaluation
@@ -388,7 +389,7 @@ def run_sim(args):
         model = model.cuda(device)
     model.eval()
 
-    sim_list = []
+    sims = []
     for wav1, wav2, truth in tqdm(test_set):
         wav1, sr1 = torchaudio.load(wav1)
         wav2, sr2 = torchaudio.load(wav2)
@@ -407,6 +408,6 @@ 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).")
-        sim_list.append(sim)
+        sims.append(sim)
 
-    return sim_list
+    return sims

src/f5_tts/infer/README.md

@@ -64,6 +64,9 @@ f5-tts_infer-cli \
 # 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>
+
+# More instructions
+f5-tts_infer-cli --help
 ```
 
 And a `.toml` file would help with more flexible usage.

src/f5_tts/infer/SHARED.md

@@ -22,12 +22,12 @@
     - [Finnish Common\_Voice Vox\_Populi @ finetune @ fi](#finnish-common_voice-vox_populi--finetune--fi)
 - [French](#french)
     - [French LibriVox @ finetune @ fr](#french-librivox--finetune--fr)
+- [Hindi](#hindi)
+    - [F5-TTS Small @ pretrain @ hi](#f5-tts-small--pretrain--hi)
 - [Italian](#italian)
     - [F5-TTS Italian @ finetune @ it](#f5-tts-italian--finetune--it)
 - [Japanese](#japanese)
     - [F5-TTS Japanese @ pretrain/finetune @ ja](#f5-tts-japanese--pretrainfinetune--ja)
-- [Hindi](#hindi)
-    - [F5-TTS Small @ pretrain @ hi](#f5-tts-small--pretrain--hi)
 - [Mandarin](#mandarin)
 - [Spanish](#spanish)
     - [F5-TTS Spanish @ pretrain/finetune @ es](#f5-tts-spanish--pretrainfinetune--es)
@@ -81,6 +81,23 @@ VOCAB_FILE: hf://RASPIAUDIO/F5-French-MixedSpeakers-reduced/vocab.txt
 - [Discussion about this training can be found here](https://github.com/SWivid/F5-TTS/issues/434).
 
 
+## Hindi
+
+#### F5-TTS Small @ pretrain @ hi
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Small|[ckpt & vocab](https://huggingface.co/SPRINGLab/F5-Hindi-24KHz)|[IndicTTS Hi](https://huggingface.co/datasets/SPRINGLab/IndicTTS-Hindi) & [IndicVoices-R Hi](https://huggingface.co/datasets/SPRINGLab/IndicVoices-R_Hindi) |cc-by-4.0|
+
+```bash
+MODEL_CKPT: hf://SPRINGLab/F5-Hindi-24KHz/model_2500000.safetensors
+VOCAB_FILE: hf://SPRINGLab/F5-Hindi-24KHz/vocab.txt
+```
+
+Authors: SPRING Lab, Indian Institute of Technology, Madras
+<br>
+Website: https://asr.iitm.ac.in/
+
+
 ## Italian
 
 #### F5-TTS Italian @ finetune @ it
@@ -110,21 +127,6 @@ MODEL_CKPT: hf://Jmica/F5TTS/JA_8500000/model_8499660.pt
 VOCAB_FILE: hf://Jmica/F5TTS/JA_8500000/vocab_updated.txt
 ```
 
-## Hindi
-
-#### F5-TTS Small @ pretrain @ hi
-|Model|🤗Hugging Face|Data (Hours)|Model License|
-|:---:|:------------:|:-----------:|:-------------:|
-|F5-TTS Small|[ckpt & vocab](https://huggingface.co/SPRINGLab/F5-Hindi-24KHz)|[IndicTTS Hi](https://huggingface.co/datasets/SPRINGLab/IndicTTS-Hindi) & [IndicVoices-R Hi](https://huggingface.co/datasets/SPRINGLab/IndicVoices-R_Hindi) |cc-by-4.0|
-
-```bash
-MODEL_CKPT: hf://SPRINGLab/F5-Hindi-24KHz/model_2500000.safetensors
-VOCAB_FILE: hf://SPRINGLab/F5-Hindi-24KHz/vocab.txt
-```
-
-Authors: SPRING Lab, Indian Institute of Technology, Madras
-<br>
-Website: https://asr.iitm.ac.in/   
 
 ## Mandarin
 

src/f5_tts/infer/examples/basic/basic.toml

@@ -8,4 +8,4 @@ gen_text = "I don't really care what you call me. I've been a silent spectator,
 gen_file = ""
 remove_silence = false
 output_dir = "tests"
-output_file = "infer_cli_out.wav"
+output_file = "infer_cli_basic.wav"

src/f5_tts/infer/examples/multi/story.toml

@@ -8,6 +8,7 @@ gen_text = ""
 gen_file = "infer/examples/multi/story.txt"
 remove_silence = true
 output_dir = "tests"
+output_file = "infer_cli_story.wav"
 
 [voices.town]
 ref_audio = "infer/examples/multi/town.flac"

src/f5_tts/infer/infer_cli.py

@@ -2,6 +2,7 @@ import argparse
 import codecs
 import os
 import re
+from datetime import datetime
 from importlib.resources import files
 from pathlib import Path
 
@@ -11,6 +12,14 @@ import tomli
 from cached_path import cached_path
 
 from f5_tts.infer.utils_infer import (
+    mel_spec_type,
+    target_rms,
+    cross_fade_duration,
+    nfe_step,
+    cfg_strength,
+    sway_sampling_coef,
+    speed,
+    fix_duration,
     infer_process,
     load_model,
     load_vocoder,
@@ -19,6 +28,7 @@ from f5_tts.infer.utils_infer import (
 )
 from f5_tts.model import DiT, UNetT
 
+
 parser = argparse.ArgumentParser(
     prog="python3 infer-cli.py",
     description="Commandline interface for E2/F5 TTS with Advanced Batch Processing.",
@@ -27,86 +37,161 @@ parser = argparse.ArgumentParser(
 parser.add_argument(
     "-c",
     "--config",
-    help="Configuration file. Default=infer/examples/basic/basic.toml",
+    type=str,
     default=os.path.join(files("f5_tts").joinpath("infer/examples/basic"), "basic.toml"),
+    help="The configuration file, default see infer/examples/basic/basic.toml",
 )
+
+
+# Note. Not to provide default value here in order to read default from config file
+
 parser.add_argument(
     "-m",
     "--model",
-    help="F5-TTS | E2-TTS",
+    type=str,
+    help="The model name: F5-TTS | E2-TTS",
 )
 parser.add_argument(
     "-p",
     "--ckpt_file",
-    help="The Checkpoint .pt",
+    type=str,
+    help="The path to model checkpoint .pt, leave blank to use default",
 )
 parser.add_argument(
     "-v",
     "--vocab_file",
-    help="The vocab .txt",
+    type=str,
+    help="The path to vocab file .txt, leave blank to use default",
+)
+parser.add_argument(
+    "-r",
+    "--ref_audio",
+    type=str,
+    help="The reference audio file.",
+)
+parser.add_argument(
+    "-s",
+    "--ref_text",
+    type=str,
+    help="The transcript/subtitle for the reference audio",
 )
-parser.add_argument("-r", "--ref_audio", type=str, help="Reference audio file < 15 seconds.")
-parser.add_argument("-s", "--ref_text", type=str, default="666", help="Subtitle for the reference audio.")
 parser.add_argument(
     "-t",
     "--gen_text",
     type=str,
-    help="Text to generate.",
+    help="The text to make model synthesize a speech",
 )
 parser.add_argument(
     "-f",
     "--gen_file",
     type=str,
-    help="File with text to generate. Ignores --gen_text",
+    help="The file with text to generate, will ignore --gen_text",
 )
 parser.add_argument(
     "-o",
     "--output_dir",
     type=str,
-    help="Path to output folder..",
+    help="The path to output folder",
 )
 parser.add_argument(
     "-w",
     "--output_file",
     type=str,
-    help="Filename of output file..",
+    help="The name of output file",
 )
 parser.add_argument(
     "--save_chunk",
     action="store_true",
-    help="Save chunk audio if your text is too long.",
+    help="To save each audio chunks during inference",
 )
 parser.add_argument(
     "--remove_silence",
-    help="Remove silence.",
+    action="store_true",
+    help="To remove long silence found in ouput",
 )
-parser.add_argument("--vocoder_name", type=str, default="vocos", choices=["vocos", "bigvgan"], help="vocoder name")
 parser.add_argument(
     "--load_vocoder_from_local",
     action="store_true",
-    help="load vocoder from local. Default: ../checkpoints/charactr/vocos-mel-24khz",
+    help="To load vocoder from local dir, default to ../checkpoints/charactr/vocos-mel-24khz",
 )
 parser.add_argument(
-    "--speed",
+    "--vocoder_name",
+    type=str,
+    choices=["vocos", "bigvgan"],
+    help=f"Used vocoder name: vocos | bigvgan, default {mel_spec_type}",
+)
+parser.add_argument(
+    "--target_rms",
+    type=float,
+    help=f"Target output speech loudness normalization value, default {target_rms}",
+)
+parser.add_argument(
+    "--cross_fade_duration",
     type=float,
-    default=1.0,
-    help="Adjust the speed of the audio generation (default: 1.0)",
+    help=f"Duration of cross-fade between audio segments in seconds, default {cross_fade_duration}",
 )
 parser.add_argument(
     "--nfe_step",
     type=int,
-    default=32,
-    help="Set the number of denoising steps (default: 32)",
+    help=f"The number of function evaluation (denoising steps), default {nfe_step}",
+)
+parser.add_argument(
+    "--cfg_strength",
+    type=float,
+    help=f"Classifier-free guidance strength, default {cfg_strength}",
+)
+parser.add_argument(
+    "--sway_sampling_coef",
+    type=float,
+    help=f"Sway Sampling coefficient, default {sway_sampling_coef}",
+)
+parser.add_argument(
+    "--speed",
+    type=float,
+    help=f"The speed of the generated audio, default {speed}",
+)
+parser.add_argument(
+    "--fix_duration",
+    type=float,
+    help=f"Fix the total duration (ref and gen audios) in seconds, default {fix_duration}",
 )
 args = parser.parse_args()
 
+
+# config file
+
 config = tomli.load(open(args.config, "rb"))
 
-ref_audio = args.ref_audio if args.ref_audio else config["ref_audio"]
-ref_text = args.ref_text if args.ref_text != "666" else config["ref_text"]
-gen_text = args.gen_text if args.gen_text else config["gen_text"]
-gen_file = args.gen_file if args.gen_file else config["gen_file"]
-save_chunk = args.save_chunk if args.save_chunk else False
+
+# command-line interface parameters
+
+model = args.model or config.get("model", "F5-TTS")
+ckpt_file = args.ckpt_file or config.get("ckpt_file", "")
+vocab_file = args.vocab_file or config.get("vocab_file", "")
+
+ref_audio = args.ref_audio or config.get("ref_audio", "infer/examples/basic/basic_ref_en.wav")
+ref_text = args.ref_text or config.get("ref_text", "Some call me nature, others call me mother nature.")
+gen_text = args.gen_text or config.get("gen_text", "Here we generate something just for test.")
+gen_file = args.gen_file or config.get("gen_file", "")
+
+output_dir = args.output_dir or config.get("output_dir", "tests")
+output_file = args.output_file or config.get(
+    "output_file", f"infer_cli_{datetime.now().strftime(r'%Y%m%d_%H%M%S')}.wav"
+)
+
+save_chunk = args.save_chunk
+remove_silence = args.remove_silence
+load_vocoder_from_local = args.load_vocoder_from_local
+
+vocoder_name = args.vocoder_name or config.get("vocoder_name", mel_spec_type)
+target_rms = args.target_rms or config.get("target_rms", target_rms)
+cross_fade_duration = args.cross_fade_duration or config.get("cross_fade_duration", cross_fade_duration)
+nfe_step = args.nfe_step or config.get("nfe_step", nfe_step)
+cfg_strength = args.cfg_strength or config.get("cfg_strength", cfg_strength)
+sway_sampling_coef = args.sway_sampling_coef or config.get("sway_sampling_coef", sway_sampling_coef)
+speed = args.speed or config.get("speed", speed)
+fix_duration = args.fix_duration or config.get("fix_duration", fix_duration)
+
 
 # patches for pip pkg user
 if "infer/examples/" in ref_audio:
@@ -119,35 +204,39 @@ if "voices" in config:
         if "infer/examples/" in voice_ref_audio:
             config["voices"][voice]["ref_audio"] = str(files("f5_tts").joinpath(f"{voice_ref_audio}"))
 
+
+# ignore gen_text if gen_file provided
+
 if gen_file:
     gen_text = codecs.open(gen_file, "r", "utf-8").read()
-output_dir = args.output_dir if args.output_dir else config["output_dir"]
-output_file = args.output_file if args.output_file else config["output_file"]
-model = args.model if args.model else config["model"]
-ckpt_file = args.ckpt_file if args.ckpt_file else ""
-vocab_file = args.vocab_file if args.vocab_file else ""
-remove_silence = args.remove_silence if args.remove_silence else config["remove_silence"]
-speed = args.speed
-nfe_step = args.nfe_step
+
+
+# output path
 
 wave_path = Path(output_dir) / output_file
 # spectrogram_path = Path(output_dir) / "infer_cli_out.png"
+if save_chunk:
+    output_chunk_dir = os.path.join(output_dir, f"{Path(output_file).stem}_chunks")
+    if not os.path.exists(output_chunk_dir):
+        os.makedirs(output_chunk_dir)
+
+
+# load vocoder
 
-vocoder_name = args.vocoder_name
-mel_spec_type = args.vocoder_name
 if vocoder_name == "vocos":
     vocoder_local_path = "../checkpoints/vocos-mel-24khz"
 elif vocoder_name == "bigvgan":
     vocoder_local_path = "../checkpoints/bigvgan_v2_24khz_100band_256x"
 
-vocoder = load_vocoder(vocoder_name=mel_spec_type, is_local=args.load_vocoder_from_local, local_path=vocoder_local_path)
+vocoder = load_vocoder(vocoder_name=vocoder_name, is_local=load_vocoder_from_local, local_path=vocoder_local_path)
+
 
+# load TTS model
 
-# load models
 if model == "F5-TTS":
     model_cls = DiT
     model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
-    if ckpt_file == "":
+    if not ckpt_file:  # path not specified, download from repo
         if vocoder_name == "vocos":
             repo_name = "F5-TTS"
             exp_name = "F5TTS_Base"
@@ -164,19 +253,21 @@ elif model == "E2-TTS":
     assert vocoder_name == "vocos", "E2-TTS only supports vocoder vocos"
     model_cls = UNetT
     model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
-    if ckpt_file == "":
+    if not ckpt_file:  # path not specified, download from repo
         repo_name = "E2-TTS"
         exp_name = "E2TTS_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
 
-
 print(f"Using {model}...")
-ema_model = load_model(model_cls, model_cfg, ckpt_file, mel_spec_type=mel_spec_type, vocab_file=vocab_file)
+ema_model = load_model(model_cls, model_cfg, ckpt_file, mel_spec_type=vocoder_name, vocab_file=vocab_file)
+
 
+# inference process
 
-def main_process(ref_audio, ref_text, text_gen, model_obj, mel_spec_type, remove_silence, speed):
+
+def main():
     main_voice = {"ref_audio": ref_audio, "ref_text": ref_text}
     if "voices" not in config:
         voices = {"main": main_voice}
@@ -184,16 +275,16 @@ def main_process(ref_audio, ref_text, text_gen, model_obj, mel_spec_type, remove
         voices = config["voices"]
         voices["main"] = main_voice
     for voice in voices:
+        print("Voice:", voice)
+        print("ref_audio ", voices[voice]["ref_audio"])
         voices[voice]["ref_audio"], voices[voice]["ref_text"] = preprocess_ref_audio_text(
             voices[voice]["ref_audio"], voices[voice]["ref_text"]
         )
-        print("Voice:", voice)
-        print("Ref_audio:", voices[voice]["ref_audio"])
-        print("Ref_text:", voices[voice]["ref_text"])
+        print("ref_audio_", voices[voice]["ref_audio"], "\n\n")
 
     generated_audio_segments = []
     reg1 = r"(?=\[\w+\])"
-    chunks = re.split(reg1, text_gen)
+    chunks = re.split(reg1, gen_text)
     reg2 = r"\[(\w+)\]"
     for text in chunks:
         if not text.strip():
@@ -208,21 +299,35 @@ def main_process(ref_audio, ref_text, text_gen, model_obj, mel_spec_type, remove
             print(f"Voice {voice} not found, using main.")
             voice = "main"
         text = re.sub(reg2, "", text)
-        gen_text = text.strip()
-        ref_audio = voices[voice]["ref_audio"]
-        ref_text = voices[voice]["ref_text"]
+        ref_audio_ = voices[voice]["ref_audio"]
+        ref_text_ = voices[voice]["ref_text"]
+        gen_text_ = text.strip()
         print(f"Voice: {voice}")
-        audio, final_sample_rate, spectragram = infer_process(
-            ref_audio,
-            ref_text,
-            gen_text,
-            model_obj,
+        audio_segment, final_sample_rate, spectragram = infer_process(
+            ref_audio_,
+            ref_text_,
+            gen_text_,
+            ema_model,
             vocoder,
-            mel_spec_type=mel_spec_type,
-            speed=speed,
+            mel_spec_type=vocoder_name,
+            target_rms=target_rms,
+            cross_fade_duration=cross_fade_duration,
             nfe_step=nfe_step,
+            cfg_strength=cfg_strength,
+            sway_sampling_coef=sway_sampling_coef,
+            speed=speed,
+            fix_duration=fix_duration,
         )
-        generated_audio_segments.append(audio)
+        generated_audio_segments.append(audio_segment)
+
+        if save_chunk:
+            if len(gen_text_) > 200:
+                gen_text_ = gen_text_[:200] + " ... "
+            sf.write(
+                os.path.join(output_chunk_dir, f"{len(generated_audio_segments)-1}_{gen_text_}.wav"),
+                audio_segment,
+                final_sample_rate,
+            )
 
     if generated_audio_segments:
         final_wave = np.concatenate(generated_audio_segments)
@@ -236,22 +341,6 @@ def main_process(ref_audio, ref_text, text_gen, model_obj, mel_spec_type, remove
             if remove_silence:
                 remove_silence_for_generated_wav(f.name)
             print(f.name)
-            # Ensure the gen_text chunk directory exists
-
-            if save_chunk:
-                gen_text_chunk_dir = os.path.join(output_dir, "chunks")
-                if not os.path.exists(gen_text_chunk_dir):  # if Not create directory
-                    os.makedirs(gen_text_chunk_dir)
-
-                # Save individual chunks as separate files
-                for idx, segment in enumerate(generated_audio_segments):
-                    gen_text_chunk_path = os.path.join(output_dir, gen_text_chunk_dir, f"chunk_{idx}.wav")
-                    sf.write(gen_text_chunk_path, segment, final_sample_rate)
-                    print(f"Saved gen_text chunk {idx} at {gen_text_chunk_path}")
-
-
-def main():
-    main_process(ref_audio, ref_text, gen_text, ema_model, mel_spec_type, remove_silence, speed)
 
 
 if __name__ == "__main__":