Create generator.py

generator.py

@@ -0,0 +1,176 @@
+from dataclasses import dataclass
+from typing import List, Tuple
+
+import torch
+import torchaudio
+from huggingface_hub import hf_hub_download
+from models import Model
+from moshi.models import loaders
+from tokenizers.processors import TemplateProcessing
+from transformers import AutoTokenizer
+from watermarking import CSM_1B_GH_WATERMARK, load_watermarker, watermark
+
+
+@dataclass
+class Segment:
+    speaker: int
+    text: str
+    # (num_samples,), sample_rate = 24_000
+    audio: torch.Tensor
+
+
+def load_llama3_tokenizer():
+    """
+    https://github.com/huggingface/transformers/issues/22794#issuecomment-2092623992
+    """
+    tokenizer_name = "meta-llama/Llama-3.2-1B"
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+    bos = tokenizer.bos_token
+    eos = tokenizer.eos_token
+    tokenizer._tokenizer.post_processor = TemplateProcessing(
+        single=f"{bos}:0 $A:0 {eos}:0",
+        pair=f"{bos}:0 $A:0 {eos}:0 {bos}:1 $B:1 {eos}:1",
+        special_tokens=[(f"{bos}", tokenizer.bos_token_id), (f"{eos}", tokenizer.eos_token_id)],
+    )
+
+    return tokenizer
+
+
+class Generator:
+    def __init__(
+        self,
+        model: Model,
+    ):
+        self._model = model
+        self._model.setup_caches(1)
+
+        self._text_tokenizer = load_llama3_tokenizer()
+
+        device = next(model.parameters()).device
+        mimi_weight = hf_hub_download(loaders.DEFAULT_REPO, loaders.MIMI_NAME)
+        mimi = loaders.get_mimi(mimi_weight, device=device)
+        mimi.set_num_codebooks(32)
+        self._audio_tokenizer = mimi
+
+        self._watermarker = load_watermarker(device=device)
+
+        self.sample_rate = mimi.sample_rate
+        self.device = device
+
+    def _tokenize_text_segment(self, text: str, speaker: int) -> Tuple[torch.Tensor, torch.Tensor]:
+        frame_tokens = []
+        frame_masks = []
+
+        text_tokens = self._text_tokenizer.encode(f"[{speaker}]{text}")
+        text_frame = torch.zeros(len(text_tokens), 33).long()
+        text_frame_mask = torch.zeros(len(text_tokens), 33).bool()
+        text_frame[:, -1] = torch.tensor(text_tokens)
+        text_frame_mask[:, -1] = True
+
+        frame_tokens.append(text_frame.to(self.device))
+        frame_masks.append(text_frame_mask.to(self.device))
+
+        return torch.cat(frame_tokens, dim=0), torch.cat(frame_masks, dim=0)
+
+    def _tokenize_audio(self, audio: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        assert audio.ndim == 1, "Audio must be single channel"
+
+        frame_tokens = []
+        frame_masks = []
+
+        # (K, T)
+        audio = audio.to(self.device)
+        audio_tokens = self._audio_tokenizer.encode(audio.unsqueeze(0).unsqueeze(0))[0]
+        # add EOS frame
+        eos_frame = torch.zeros(audio_tokens.size(0), 1).to(self.device)
+        audio_tokens = torch.cat([audio_tokens, eos_frame], dim=1)
+
+        audio_frame = torch.zeros(audio_tokens.size(1), 33).long().to(self.device)
+        audio_frame_mask = torch.zeros(audio_tokens.size(1), 33).bool().to(self.device)
+        audio_frame[:, :-1] = audio_tokens.transpose(0, 1)
+        audio_frame_mask[:, :-1] = True
+
+        frame_tokens.append(audio_frame)
+        frame_masks.append(audio_frame_mask)
+
+        return torch.cat(frame_tokens, dim=0), torch.cat(frame_masks, dim=0)
+
+    def _tokenize_segment(self, segment: Segment) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Returns:
+            (seq_len, 33), (seq_len, 33)
+        """
+        text_tokens, text_masks = self._tokenize_text_segment(segment.text, segment.speaker)
+        audio_tokens, audio_masks = self._tokenize_audio(segment.audio)
+
+        return torch.cat([text_tokens, audio_tokens], dim=0), torch.cat([text_masks, audio_masks], dim=0)
+
+    @torch.inference_mode()
+    def generate(
+        self,
+        text: str,
+        speaker: int,
+        context: List[Segment],
+        max_audio_length_ms: float = 90_000,
+        temperature: float = 0.9,
+        topk: int = 50,
+    ) -> torch.Tensor:
+        self._model.reset_caches()
+
+        max_generation_len = int(max_audio_length_ms / 80)
+        tokens, tokens_mask = [], []
+        for segment in context:
+            segment_tokens, segment_tokens_mask = self._tokenize_segment(segment)
+            tokens.append(segment_tokens)
+            tokens_mask.append(segment_tokens_mask)
+
+        gen_segment_tokens, gen_segment_tokens_mask = self._tokenize_text_segment(text, speaker)
+        tokens.append(gen_segment_tokens)
+        tokens_mask.append(gen_segment_tokens_mask)
+
+        prompt_tokens = torch.cat(tokens, dim=0).long().to(self.device)
+        prompt_tokens_mask = torch.cat(tokens_mask, dim=0).bool().to(self.device)
+
+        samples = []
+        curr_tokens = prompt_tokens.unsqueeze(0)
+        curr_tokens_mask = prompt_tokens_mask.unsqueeze(0)
+        curr_pos = torch.arange(0, prompt_tokens.size(0)).unsqueeze(0).long().to(self.device)
+
+        max_seq_len = 2048
+        max_context_len = max_seq_len - max_generation_len
+        if curr_tokens.size(1) >= max_context_len:
+            raise ValueError(
+                f"Inputs too long, must be below max_seq_len - max_generation_len: {max_context_len}"
+            )
+
+        for _ in range(max_generation_len):
+            sample = self._model.generate_frame(curr_tokens, curr_tokens_mask, curr_pos, temperature, topk)
+            if torch.all(sample == 0):
+                break  # eos
+
+            samples.append(sample)
+
+            curr_tokens = torch.cat([sample, torch.zeros(1, 1).long().to(self.device)], dim=1).unsqueeze(1)
+            curr_tokens_mask = torch.cat(
+                [torch.ones_like(sample).bool(), torch.zeros(1, 1).bool().to(self.device)], dim=1
+            ).unsqueeze(1)
+            curr_pos = curr_pos[:, -1:] + 1
+
+        audio = self._audio_tokenizer.decode(torch.stack(samples).permute(1, 2, 0)).squeeze(0).squeeze(0)
+
+        # This applies an imperceptible watermark to identify audio as AI-generated.
+        # Watermarking ensures transparency, dissuades misuse, and enables traceability.
+        # Please be a responsible AI citizen and keep the watermarking in place.
+        # If using CSM 1B in another application, use your own private key and keep it secret.
+        audio, wm_sample_rate = watermark(self._watermarker, audio, self.sample_rate, CSM_1B_GH_WATERMARK)
+        audio = torchaudio.functional.resample(audio, orig_freq=wm_sample_rate, new_freq=self.sample_rate)
+
+        return audio
+
+
+def load_csm_1b(device: str = "cuda") -> Generator:
+    model = Model.from_pretrained("sesame/csm-1b")
+    model.to(device=device, dtype=torch.bfloat16)
+
+    generator = Generator(model)
+    return generator