| --- |
| license: mit |
| language: |
| - ar |
| - da |
| - de |
| - el |
| - en |
| - es |
| - fi |
| - fr |
| - he |
| - hi |
| - it |
| - ja |
| - ko |
| - ms |
| - nl |
| - 'no' |
| - pl |
| - pt |
| - ru |
| - sv |
| - sw |
| - tr |
| - zh |
| pipeline_tag: text-to-speech |
| tags: |
| - text-to-speech |
| - speech |
| - speech-generation |
| - voice-cloning |
| - multilingual-tts |
| library_name: chatterbox |
| base_model: |
| - ResembleAI/chatterbox |
| --- |
| |
| <img width="800" alt="cb-big2" src="https://github.com/user-attachments/assets/bd8c5f03-e91d-4ee5-b680-57355da204d1" /> |
|
|
| <h1 style="font-size: 32px">Chatterbox TTS</h1> |
|
|
| <div style="display: flex; align-items: center; gap: 12px"> |
| <a href="https://resemble-ai.github.io/chatterbox_demopage/"> |
| <img src="https://img.shields.io/badge/listen-demo_samples-blue" alt="Listen to Demo Samples" /> |
| </a> |
| <a href="https://huggingface.co/spaces/ResembleAI/Chatterbox"> |
| <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg" alt="Open in HF Spaces" /> |
| </a> |
| <a href="https://podonos.com/resembleai/chatterbox"> |
| <img src="https://static-public.podonos.com/badges/insight-on-pdns-sm-dark.svg" alt="Insight on Podos" /> |
| </a> |
| </div> |
| |
| <div style="display: flex; align-items: center; gap: 8px;"> |
| <img width="100" alt="resemble-logo-horizontal" src="https://github.com/user-attachments/assets/35cf756b-3506-4943-9c72-c05ddfa4e525" /> |
| </div> |
|
|
| **Chatterbox Multilingual** [Resemble AI's](https://resemble.ai) production-grade open source TTS model. Chatterbox Multilingual supports **Arabic**, **Danish**, **German**, **Greek**, **English**, **Spanish**, **Finnish**, **French**, **Hebrew**, **Hindi**, **Italian**, **Japanese**, **Korean**, **Malay**, **Dutch**, **Norwegian**, **Polish**, **Portuguese**, **Russian**, **Swedish**, **Swahili**, **Turkish**, **Chinese** out of the box. Licensed under MIT, Chatterbox has been benchmarked against leading closed-source systems like ElevenLabs, and is consistently preferred in side-by-side evaluations. |
|
|
| Whether you're working on memes, videos, games, or AI agents, Chatterbox brings your content to life. It's also the first open source TTS model to support **emotion exaggeration control**, a powerful feature that makes your voices stand out. |
|
|
| Chatterbox is provided in an exported ONNX format, enabling fast and portable inference with ONNX Runtime across platforms. |
|
|
| # Key Details |
| - SoTA zeroshot English TTS |
| - 0.5B Llama backbone |
| - Unique exaggeration/intensity control |
| - Ultra-stable with alignment-informed inference |
| - Trained on 0.5M hours of cleaned data |
| - Watermarked outputs (optional) |
| - Easy voice conversion script using onnxruntime |
| - [Outperforms ElevenLabs](https://podonos.com/resembleai/chatterbox) |
|
|
| # Tips |
| - **General Use (TTS and Voice Agents):** |
| - The default settings (`exaggeration=0.5`, `cfg=0.5`) work well for most prompts. |
|
|
| - **Expressive or Dramatic Speech:** |
| - Try increase `exaggeration` to around `0.7` or higher. |
| - Higher `exaggeration` tends to speed up speech; |
|
|
|
|
| # Usage |
| [Link to GitHub ONNX Export and Inference script](https://github.com/VladOS95-cyber/onnx_conversion_scripts/tree/main/chatterbox) |
|
|
| ```python |
| # !pip install --upgrade onnxruntime==1.22.1 huggingface_hub==0.34.4 transformers==4.46.3 numpy==2.2.6 tqdm==4.67.1 librosa==0.11.0 soundfile==0.13.1 resemble-perth==1.0.1 |
| # for Chinese, Japanese additionally pip install pkuseg==0.0.25 pykakasi==2.3.0 |
| |
| import onnxruntime |
| |
| from huggingface_hub import hf_hub_download |
| from transformers import AutoTokenizer |
| |
| import numpy as np |
| from tqdm import tqdm |
| import librosa |
| import soundfile as sf |
| from unicodedata import category |
| import json |
| |
| S3GEN_SR = 24000 |
| START_SPEECH_TOKEN = 6561 |
| STOP_SPEECH_TOKEN = 6562 |
| 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", |
| } |
| |
| |
| class RepetitionPenaltyLogitsProcessor: |
| def __init__(self, penalty: float): |
| if not isinstance(penalty, float) or not (penalty > 0): |
| raise ValueError(f"`penalty` must be a strictly positive float, but is {penalty}") |
| self.penalty = penalty |
| |
| def __call__(self, input_ids: np.ndarray, scores: np.ndarray) -> np.ndarray: |
| score = np.take_along_axis(scores, input_ids, axis=1) |
| score = np.where(score < 0, score * self.penalty, score / self.penalty) |
| scores_processed = scores.copy() |
| np.put_along_axis(scores_processed, input_ids, score, axis=1) |
| return scores_processed |
| |
| |
| class ChineseCangjieConverter: |
| """Converts Chinese characters to Cangjie codes for tokenization.""" |
| |
| def __init__(self): |
| self.word2cj = {} |
| self.cj2word = {} |
| self.segmenter = None |
| self._load_cangjie_mapping() |
| self._init_segmenter() |
| |
| def _load_cangjie_mapping(self): |
| """Load Cangjie mapping from HuggingFace model repository.""" |
| try: |
| cangjie_file = hf_hub_download( |
| repo_id="onnx-community/chatterbox-multilingual-ONNX", |
| filename="Cangjie5_TC.json", |
| ) |
| |
| with open(cangjie_file, "r", encoding="utf-8") as fp: |
| data = json.load(fp) |
| |
| for entry in data: |
| word, code = entry.split("\t")[:2] |
| self.word2cj[word] = code |
| if code not in self.cj2word: |
| self.cj2word[code] = [word] |
| else: |
| self.cj2word[code].append(word) |
| |
| except Exception as e: |
| print(f"Could not load Cangjie mapping: {e}") |
| |
| def _init_segmenter(self): |
| """Initialize pkuseg segmenter.""" |
| try: |
| from pkuseg import pkuseg |
| self.segmenter = pkuseg() |
| except ImportError: |
| print("pkuseg not available - Chinese segmentation will be skipped") |
| self.segmenter = None |
| |
| def _cangjie_encode(self, glyph: str): |
| """Encode a single Chinese glyph to Cangjie code.""" |
| normed_glyph = glyph |
| code = self.word2cj.get(normed_glyph, None) |
| if code is None: # e.g. Japanese hiragana |
| return None |
| index = self.cj2word[code].index(normed_glyph) |
| index = str(index) if index > 0 else "" |
| return code + str(index) |
| |
| |
| |
| def __call__(self, text): |
| """Convert Chinese characters in text to Cangjie tokens.""" |
| output = [] |
| if self.segmenter is not None: |
| segmented_words = self.segmenter.cut(text) |
| full_text = " ".join(segmented_words) |
| else: |
| full_text = text |
| |
| for t in full_text: |
| if category(t) == "Lo": |
| cangjie = self._cangjie_encode(t) |
| if cangjie is None: |
| output.append(t) |
| continue |
| code = [] |
| for c in cangjie: |
| code.append(f"[cj_{c}]") |
| code.append("[cj_.]") |
| code = "".join(code) |
| output.append(code) |
| else: |
| output.append(t) |
| return "".join(output) |
| |
| |
| def is_kanji(c: str) -> bool: |
| """Check if character is kanji.""" |
| return 19968 <= ord(c) <= 40959 |
| |
| |
| def is_katakana(c: str) -> bool: |
| """Check if character is katakana.""" |
| return 12449 <= ord(c) <= 12538 |
| |
| |
| def hiragana_normalize(text: str) -> str: |
| """Japanese text normalization: converts kanji to hiragana; katakana remains the same.""" |
| global _kakasi |
| |
| try: |
| if _kakasi is None: |
| import pykakasi |
| _kakasi = pykakasi.kakasi() |
| |
| result = _kakasi.convert(text) |
| out = [] |
| |
| for r in result: |
| inp = r['orig'] |
| hira = r["hira"] |
| |
| # Any kanji in the phrase |
| if any([is_kanji(c) for c in inp]): |
| if hira and hira[0] in ["は", "へ"]: # Safety check for empty hira |
| hira = " " + hira |
| out.append(hira) |
| |
| # All katakana |
| elif all([is_katakana(c) for c in inp]) if inp else False: # Safety check for empty inp |
| out.append(r['orig']) |
| |
| else: |
| out.append(inp) |
| |
| normalized_text = "".join(out) |
| |
| # Decompose Japanese characters for tokenizer compatibility |
| import unicodedata |
| normalized_text = unicodedata.normalize('NFKD', normalized_text) |
| |
| return normalized_text |
| |
| except ImportError: |
| print("pykakasi not available - Japanese text processing skipped") |
| return text |
| |
| |
| def add_hebrew_diacritics(text: str) -> str: |
| """Hebrew text normalization: adds diacritics to Hebrew text.""" |
| global _dicta |
| |
| try: |
| if _dicta is None: |
| from dicta_onnx import Dicta |
| _dicta = Dicta() |
| |
| return _dicta.add_diacritics(text) |
| |
| except ImportError: |
| print("dicta_onnx not available - Hebrew text processing skipped") |
| return text |
| except Exception as e: |
| print(f"Hebrew diacritization failed: {e}") |
| return text |
| |
| |
| def korean_normalize(text: str) -> str: |
| """Korean text normalization: decompose syllables into Jamo for tokenization.""" |
| |
| def decompose_hangul(char): |
| """Decompose Korean syllable into Jamo components.""" |
| if not ('\uac00' <= char <= '\ud7af'): |
| return char |
| |
| # Hangul decomposition formula |
| base = ord(char) - 0xAC00 |
| initial = chr(0x1100 + base // (21 * 28)) |
| medial = chr(0x1161 + (base % (21 * 28)) // 28) |
| final = chr(0x11A7 + base % 28) if base % 28 > 0 else '' |
| |
| return initial + medial + final |
| |
| # Decompose syllables and normalize punctuation |
| result = ''.join(decompose_hangul(char) for char in text) |
| return result.strip() |
| |
| |
| def prepare_language(txt, language_id): |
| # Language-specific text processing |
| cangjie_converter = ChineseCangjieConverter() |
| if language_id == 'zh': |
| txt = cangjie_converter(txt) |
| elif language_id == 'ja': |
| txt = hiragana_normalize(txt) |
| elif language_id == 'he': |
| txt = add_hebrew_diacritics(txt) |
| elif language_id == 'ko': |
| txt = korean_normalize(txt) |
| |
| # Prepend language token |
| if language_id: |
| txt = f"[{language_id.lower()}]{txt}" |
| return txt |
| |
| |
| def run_inference( |
| text="The Lord of the Rings is the greatest work of literature.", |
| language_id="en", |
| target_voice_path=None, |
| max_new_tokens=256, |
| exaggeration=0.5, |
| output_dir="converted", |
| output_file_name="output.wav", |
| apply_watermark=True, |
| ): |
| # 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}" |
| ) |
| model_id = "onnx-community/chatterbox-multilingual-ONNX" |
| if not target_voice_path: |
| target_voice_path = hf_hub_download(repo_id=model_id, filename="default_voice.wav", local_dir=output_dir) |
| |
| ## Load model |
| speech_encoder_path = hf_hub_download(repo_id=model_id, filename="speech_encoder.onnx", local_dir=output_dir, subfolder='onnx') |
| hf_hub_download(repo_id=model_id, filename="speech_encoder.onnx_data", local_dir=output_dir, subfolder='onnx') |
| embed_tokens_path = hf_hub_download(repo_id=model_id, filename="embed_tokens.onnx", local_dir=output_dir, subfolder='onnx') |
| hf_hub_download(repo_id=model_id, filename="embed_tokens.onnx_data", local_dir=output_dir, subfolder='onnx') |
| conditional_decoder_path = hf_hub_download(repo_id=model_id, filename="conditional_decoder.onnx", local_dir=output_dir, subfolder='onnx') |
| hf_hub_download(repo_id=model_id, filename="conditional_decoder.onnx_data", local_dir=output_dir, subfolder='onnx') |
| language_model_path = hf_hub_download(repo_id=model_id, filename="language_model.onnx", local_dir=output_dir, subfolder='onnx') |
| hf_hub_download(repo_id=model_id, filename="language_model.onnx_data", local_dir=output_dir, subfolder='onnx') |
| |
| # # Start inferense sessions |
| speech_encoder_session = onnxruntime.InferenceSession(speech_encoder_path) |
| embed_tokens_session = onnxruntime.InferenceSession(embed_tokens_path) |
| llama_with_past_session = onnxruntime.InferenceSession(language_model_path) |
| cond_decoder_session = onnxruntime.InferenceSession(conditional_decoder_path) |
| |
| def execute_text_to_audio_inference(text): |
| print("Start inference script...") |
| |
| audio_values, _ = librosa.load(target_voice_path, sr=S3GEN_SR) |
| audio_values = audio_values[np.newaxis, :].astype(np.float32) |
| |
| ## Prepare input |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| text = prepare_language(text, language_id) |
| input_ids = tokenizer(text, return_tensors="np")["input_ids"].astype(np.int64) |
| |
| position_ids = np.where( |
| input_ids >= START_SPEECH_TOKEN, |
| 0, |
| np.arange(input_ids.shape[1])[np.newaxis, :] - 1 |
| ) |
| |
| ort_embed_tokens_inputs = { |
| "input_ids": input_ids, |
| "position_ids": position_ids.astype(np.int64), |
| "exaggeration": np.array([exaggeration], dtype=np.float32) |
| } |
| |
| ## Instantiate the logits processors. |
| repetition_penalty = 1.2 |
| repetition_penalty_processor = RepetitionPenaltyLogitsProcessor(penalty=repetition_penalty) |
| |
| num_hidden_layers = 30 |
| num_key_value_heads = 16 |
| head_dim = 64 |
| |
| generate_tokens = np.array([[START_SPEECH_TOKEN]]) |
| |
| # ---- Generation Loop using kv_cache ---- |
| for i in tqdm(range(max_new_tokens), desc="Sampling", dynamic_ncols=True): |
| |
| inputs_embeds = embed_tokens_session.run(None, ort_embed_tokens_inputs)[0] |
| if i == 0: |
| ort_speech_encoder_input = { |
| "audio_values": audio_values, |
| } |
| cond_emb, prompt_token, ref_x_vector, prompt_feat = speech_encoder_session.run(None, ort_speech_encoder_input) |
| inputs_embeds = np.concatenate((cond_emb, inputs_embeds), axis=1) |
| |
| ## Prepare llm inputs |
| batch_size, seq_len, _ = inputs_embeds.shape |
| past_key_values = { |
| f"past_key_values.{layer}.{kv}": np.zeros([batch_size, num_key_value_heads, 0, head_dim], dtype=np.float32) |
| for layer in range(num_hidden_layers) |
| for kv in ("key", "value") |
| } |
| attention_mask = np.ones((batch_size, seq_len), dtype=np.int64) |
| logits, *present_key_values = llama_with_past_session.run(None, dict( |
| inputs_embeds=inputs_embeds, |
| attention_mask=attention_mask, |
| **past_key_values, |
| )) |
| |
| logits = logits[:, -1, :] |
| next_token_logits = repetition_penalty_processor(generate_tokens, logits) |
| |
| next_token = np.argmax(next_token_logits, axis=-1, keepdims=True).astype(np.int64) |
| generate_tokens = np.concatenate((generate_tokens, next_token), axis=-1) |
| if (next_token.flatten() == STOP_SPEECH_TOKEN).all(): |
| break |
| |
| # Get embedding for the new token. |
| position_ids = np.full( |
| (input_ids.shape[0], 1), |
| i + 1, |
| dtype=np.int64, |
| ) |
| ort_embed_tokens_inputs["input_ids"] = next_token |
| ort_embed_tokens_inputs["position_ids"] = position_ids |
| |
| ## Update values for next generation loop |
| attention_mask = np.concatenate([attention_mask, np.ones((batch_size, 1), dtype=np.int64)], axis=1) |
| for j, key in enumerate(past_key_values): |
| past_key_values[key] = present_key_values[j] |
| |
| speech_tokens = generate_tokens[:, 1:-1] |
| speech_tokens = np.concatenate([prompt_token, speech_tokens], axis=1) |
| return speech_tokens, ref_x_vector, prompt_feat |
| |
| speech_tokens, speaker_embeddings, speaker_features = execute_text_to_audio_inference(text) |
| cond_incoder_input = { |
| "speech_tokens": speech_tokens, |
| "speaker_embeddings": speaker_embeddings, |
| "speaker_features": speaker_features, |
| } |
| wav = cond_decoder_session.run(None, cond_incoder_input)[0] |
| wav = np.squeeze(wav, axis=0) |
| |
| # Optional: Apply watermark |
| if apply_watermark: |
| import perth |
| watermarker = perth.PerthImplicitWatermarker() |
| wav = watermarker.apply_watermark(wav, sample_rate=S3GEN_SR) |
| |
| sf.write(output_file_name, wav, S3GEN_SR) |
| print(f"{output_file_name} was successfully saved") |
| |
| if __name__ == "__main__": |
| run_inference( |
| text="Bonjour, comment ça va? Ceci est le modèle de synthèse vocale multilingue Chatterbox, il prend en charge 23 langues.", |
| language_id="fr", |
| exaggeration=0.5, |
| output_file_name="output.wav", |
| apply_watermark=False, |
| ) |
| |
| ``` |
|
|
|
|
| # Acknowledgements |
| - [Xenova](https://huggingface.co/Xenova) |
| - [Vladislav Bronzov](https://github.com/VladOS95-cyber) |
| - [Resemble AI](https://github.com/resemble-ai/chatterbox) |
|
|
| # Built-in PerTh Watermarking for Responsible AI |
|
|
| Every audio file generated by Chatterbox includes [Resemble AI's Perth (Perceptual Threshold) Watermarker](https://github.com/resemble-ai/perth) - imperceptible neural watermarks that survive MP3 compression, audio editing, and common manipulations while maintaining nearly 100% detection accuracy. |
|
|
| # Disclaimer |
| Don't use this model to do bad things. Prompts are sourced from freely available data on the internet. |
