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+=======
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README.md

@@ -0,0 +1,497 @@
+---
+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.

configuration.json

@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "others", "allow_remote": true}

generation_config.json

@@ -0,0 +1,8 @@
+{
+  "bos_token_id": 1,
+  "eos_token_id": [
+    2,
+    6562
+  ],
+  "repetition_penalty": 1.2
+}

tokenizer_config.json

@@ -0,0 +1,12 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": true,
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}