public/model-chatterbox-multilingual-onnx
62
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
fe912caadd
model-chatterbox-multilingu.../README.md
Cherrytest de6c1b54d7 Upload folder using ModelScope SDK (#3) 
- Upload folder using ModelScope SDK (0da188e00409b944821ca656059d99535ff66432)
2025-09-27 02:46:45 +00:00

495 lines
18 KiB
Markdown
Raw Blame History

---
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
---
<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 perth==1.0.0
# 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.
Reference in New Issue View Git Blame Copy Permalink