Create README.md

README.md

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+---
+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;">
+  <span style="font-style: italic;white-space: pre-wrap">Made with  ❤️  by</span>
+  <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.
+
+
+# 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
+[ONNX Export and Inference script](https://github.com/VladOS95-cyber/onnx_conversion_scripts/tree/main/chatterbox)
+
+```python
+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
+
+# Sampling rate of the inputs to S3TokenizerV2
+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.