from dataclasses import dataclass from pathlib import Path import os import librosa import torch import perth import torch.nn.functional as F from safetensors.torch import load_file as load_safetensors from huggingface_hub import snapshot_download from .models.t3 import T3 from .models.t3.modules.t3_config import T3Config from .models.s3tokenizer import S3_SR, drop_invalid_tokens from .models.s3gen import S3GEN_SR, S3Gen from .models.tokenizers import MTLTokenizer from .models.voice_encoder import VoiceEncoder from .models.t3.modules.cond_enc import T3Cond REPO_ID = "ResembleAI/chatterbox" # Supported languages for the multilingual model SUPPORTED_LANGUAGES = { "ar": "Arabic", "da": "Danish", "de": "German", "el": "Greek", "en": "English", "es": "Spanish", "fi": "Finnish", "fr": "French", "he": "Hebrew", "hi": "Hindi", "it": "Italian", "ja": "Japanese", "ko": "Korean", "ms": "Malay", "nl": "Dutch", "no": "Norwegian", "pl": "Polish", "pt": "Portuguese", "ru": "Russian", "sv": "Swedish", "sw": "Swahili", "tr": "Turkish", "zh": "Chinese", } def punc_norm(text: str) -> str: """ Quick cleanup func for punctuation from LLMs or containing chars not seen often in the dataset """ if len(text) == 0: return "You need to add some text for me to talk." # Capitalise first letter if text[0].islower(): text = text[0].upper() + text[1:] # Remove multiple space chars text = " ".join(text.split()) # Replace uncommon/llm punc punc_to_replace = [ ("...", ", "), ("…", ", "), (":", ","), (" - ", ", "), (";", ", "), ("—", "-"), ("–", "-"), (" ,", ","), ("“", "\""), ("”", "\""), ("‘", "'"), ("’", "'"), ] for old_char_sequence, new_char in punc_to_replace: text = text.replace(old_char_sequence, new_char) # Add full stop if no ending punc text = text.rstrip(" ") sentence_enders = {".", "!", "?", "-", ",","、","，","。","？","！"} if not any(text.endswith(p) for p in sentence_enders): text += "." return text @dataclass class Conditionals: """ Conditionals for T3 and S3Gen - T3 conditionals: - speaker_emb - clap_emb - cond_prompt_speech_tokens - cond_prompt_speech_emb - emotion_adv - S3Gen conditionals: - prompt_token - prompt_token_len - prompt_feat - prompt_feat_len - embedding """ t3: T3Cond gen: dict def to(self, device): self.t3 = self.t3.to(device=device) for k, v in self.gen.items(): if torch.is_tensor(v): self.gen[k] = v.to(device=device) return self def save(self, fpath: Path): arg_dict = dict( t3=self.t3.__dict__, gen=self.gen ) torch.save(arg_dict, fpath) @classmethod def load(cls, fpath, map_location="cpu"): kwargs = torch.load(fpath, map_location=map_location, weights_only=True) return cls(T3Cond(**kwargs['t3']), kwargs['gen']) class ChatterboxMultilingualTTS: ENC_COND_LEN = 6 * S3_SR DEC_COND_LEN = 10 * S3GEN_SR def __init__( self, t3: T3, s3gen: S3Gen, ve: VoiceEncoder, tokenizer: MTLTokenizer, device: str, conds: Conditionals = None, ): self.sr = S3GEN_SR # sample rate of synthesized audio self.t3 = t3 self.s3gen = s3gen self.ve = ve self.tokenizer = tokenizer self.device = device self.conds = conds self.watermarker = perth.PerthImplicitWatermarker() @classmethod def get_supported_languages(cls): """Return dictionary of supported language codes and names.""" return SUPPORTED_LANGUAGES.copy() @classmethod def from_local(cls, ckpt_dir, device) -> 'ChatterboxMultilingualTTS': ckpt_dir = Path(ckpt_dir) ve = VoiceEncoder() ve.load_state_dict( torch.load(ckpt_dir / "ve.pt", weights_only=True) ) ve.to(device).eval() t3 = T3(T3Config.multilingual()) t3_state = load_safetensors(ckpt_dir / "t3_23lang.safetensors") if "model" in t3_state.keys(): t3_state = t3_state["model"][0] t3.load_state_dict(t3_state) t3.to(device).eval() s3gen = S3Gen() s3gen.load_state_dict( torch.load(ckpt_dir / "s3gen.pt", weights_only=True) ) s3gen.to(device).eval() tokenizer = MTLTokenizer( str(ckpt_dir / "mtl_tokenizer.json") ) conds = None if (builtin_voice := ckpt_dir / "conds.pt").exists(): conds = Conditionals.load(builtin_voice).to(device) return cls(t3, s3gen, ve, tokenizer, device, conds=conds) @classmethod def from_pretrained(cls, device: torch.device) -> 'ChatterboxMultilingualTTS': ckpt_dir = Path( snapshot_download( repo_id=REPO_ID, repo_type="model", revision="main", allow_patterns=["ve.pt", "t3_23lang.safetensors", "s3gen.pt", "mtl_tokenizer.json", "conds.pt", "Cangjie5_TC.json"], token=os.getenv("HF_TOKEN"), ) ) return cls.from_local(ckpt_dir, device) def prepare_conditionals(self, wav_fpath, exaggeration=0.5): ## Load reference wav s3gen_ref_wav, _sr = librosa.load(wav_fpath, sr=S3GEN_SR) ref_16k_wav = librosa.resample(s3gen_ref_wav, orig_sr=S3GEN_SR, target_sr=S3_SR) s3gen_ref_wav = s3gen_ref_wav[:self.DEC_COND_LEN] s3gen_ref_dict = self.s3gen.embed_ref(s3gen_ref_wav, S3GEN_SR, device=self.device) # Speech cond prompt tokens t3_cond_prompt_tokens = None if plen := self.t3.hp.speech_cond_prompt_len: s3_tokzr = self.s3gen.tokenizer t3_cond_prompt_tokens, _ = s3_tokzr.forward([ref_16k_wav[:self.ENC_COND_LEN]], max_len=plen) t3_cond_prompt_tokens = torch.atleast_2d(t3_cond_prompt_tokens).to(self.device) # Voice-encoder speaker embedding ve_embed = torch.from_numpy(self.ve.embeds_from_wavs([ref_16k_wav], sample_rate=S3_SR)) ve_embed = ve_embed.mean(axis=0, keepdim=True).to(self.device) t3_cond = T3Cond( speaker_emb=ve_embed, cond_prompt_speech_tokens=t3_cond_prompt_tokens, emotion_adv=exaggeration * torch.ones(1, 1, 1), ).to(device=self.device) self.conds = Conditionals(t3_cond, s3gen_ref_dict) def generate( self, text, language_id, audio_prompt_path=None, exaggeration=0.5, cfg_weight=0.5, temperature=0.8, repetition_penalty=2.0, min_p=0.05, top_p=1.0, ): # Validate language_id if language_id and language_id.lower() not in SUPPORTED_LANGUAGES: supported_langs = ", ".join(SUPPORTED_LANGUAGES.keys()) raise ValueError( f"Unsupported language_id '{language_id}'. " f"Supported languages: {supported_langs}" ) if audio_prompt_path: self.prepare_conditionals(audio_prompt_path, exaggeration=exaggeration) else: assert self.conds is not None, "Please `prepare_conditionals` first or specify `audio_prompt_path`" # Update exaggeration if needed if float(exaggeration) != float(self.conds.t3.emotion_adv[0, 0, 0].item()): _cond: T3Cond = self.conds.t3 self.conds.t3 = T3Cond( speaker_emb=_cond.speaker_emb, cond_prompt_speech_tokens=_cond.cond_prompt_speech_tokens, emotion_adv=exaggeration * torch.ones(1, 1, 1), ).to(device=self.device) # Norm and tokenize text text = punc_norm(text) text_tokens = self.tokenizer.text_to_tokens(text, language_id=language_id.lower() if language_id else None).to(self.device) text_tokens = torch.cat([text_tokens, text_tokens], dim=0) # Need two seqs for CFG sot = self.t3.hp.start_text_token eot = self.t3.hp.stop_text_token text_tokens = F.pad(text_tokens, (1, 0), value=sot) text_tokens = F.pad(text_tokens, (0, 1), value=eot) with torch.inference_mode(): speech_tokens = self.t3.inference( t3_cond=self.conds.t3, text_tokens=text_tokens, max_new_tokens=1000, # TODO: use the value in config temperature=temperature, cfg_weight=cfg_weight, repetition_penalty=repetition_penalty, min_p=min_p, top_p=top_p, ) # Extract only the conditional batch. speech_tokens = speech_tokens[0] # TODO: output becomes 1D speech_tokens = drop_invalid_tokens(speech_tokens) speech_tokens = speech_tokens.to(self.device) wav, _ = self.s3gen.inference( speech_tokens=speech_tokens, ref_dict=self.conds.gen, ) wav = wav.squeeze(0).detach().cpu().numpy() watermarked_wav = self.watermarker.apply_watermark(wav, sample_rate=self.sr) return torch.from_numpy(watermarked_wav).unsqueeze(0)