import os from dataclasses import dataclass from typing import List, Tuple import torch import torchaudio from huggingface_hub import hf_hub_download from models import Model, ModelArgs from moshi.models import loaders from tokenizers.processors import TemplateProcessing from transformers import AutoTokenizer from watermarking import load_watermarker, watermark CSM_1B_HF_WATERMARK = list(map(int, os.getenv("WATERMARK_KEY").split(" "))) @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]: 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_audio_frames = 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_audio_frames if curr_tokens.size(1) >= max_seq_len: raise ValueError(f"Inputs too long, must be below max_seq_len - max_audio_frames: {max_seq_len}") for _ in range(max_audio_frames): 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_HF_WATERMARK) audio = torchaudio.functional.resample(audio, orig_freq=wm_sample_rate, new_freq=self.sample_rate) return audio def load_csm_1b(ckpt_path: str = "ckpt.pt", device: str = "cuda") -> Generator: model_args = ModelArgs( backbone_flavor="llama-1B", decoder_flavor="llama-100M", text_vocab_size=128256, audio_vocab_size=2051, audio_num_codebooks=32, ) model = Model(model_args).to(device=device, dtype=torch.bfloat16) state_dict = torch.load(ckpt_path) model.load_state_dict(state_dict) generator = Generator(model) return generator