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---
license: mit
language:
- en
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** [Resemble AI's](https://resemble.ai) production-grade open source TTS model. Chatterbox supports **English** 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
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
S3GEN_SR = 24000
START_SPEECH_TOKEN = 6561
STOP_SPEECH_TOKEN = 6562
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
def run_inference(
text="The Lord of the Rings is the greatest work of literature.",
target_voice_path=None,
max_new_tokens = 256,
exaggeration=0.5,
output_dir="converted",
output_file_name="output.wav",
apply_watermark=True,
):
model_id = "onnx-community/chatterbox-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)
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,
"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]], dtype=np.long)
# ---- 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)
llm_position_ids = np.cumsum(attention_mask, axis=1, dtype=np.int64) - 1
logits, *present_key_values = llama_with_past_session.run(None, dict(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
position_ids=llm_position_ids,
**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)
llm_position_ids = llm_position_ids[:, -1:] + 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="Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill.",
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. |