init commit

.gitattributes

@@ -33,3 +33,11 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+f5_tts/infer/examples/basic/basic_ref_en.wav filter=lfs diff=lfs merge=lfs -text
+f5_tts/infer/examples/basic/basic_ref_zh.wav filter=lfs diff=lfs merge=lfs -text
+f5_tts/infer/examples/multi/country.flac filter=lfs diff=lfs merge=lfs -text
+f5_tts/infer/examples/multi/main.flac filter=lfs diff=lfs merge=lfs -text
+f5_tts/infer/examples/multi/town.flac filter=lfs diff=lfs merge=lfs -text
+prompts/MAR_F_HAPPY_00001.wav filter=lfs diff=lfs merge=lfs -text
+prompts/PAN_F_HAPPY_00001.wav filter=lfs diff=lfs merge=lfs -text
+samples/namaste.wav filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,98 @@
 ---
 license: apache-2.0
 ---
+
+---
+
+datasets:
+
+- ai4bharat/indicvoices_r
+- ai4bharat/Rasa
+  language:
+- as
+- bn
+- gu
+- mr
+- hi
+- kn
+- ml
+- or
+- pa
+- ta
+- te
+  pipeline_tag: text-to-speech
+
+---
+
+# **IndicF5: High-Quality Text-to-Speech for Indian Languages**
+
+We release **IndicF5**, a **near-human polyglot** **Text-to-Speech (TTS)** model trained on **1417 hours** of high-quality speech from **[Rasa](https://huggingface.co/datasets/ai4bharat/Rasa), [IndicTTS](https://www.iitm.ac.in/donlab/indictts/database), [LIMMITS](https://sites.google.com/view/limmits24/), and [IndicVoices-R](https://huggingface.co/datasets/ai4bharat/indicvoices_r)**.
+
+IndicF5 supports **11 Indian languages**:  
+**Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, Telugu.**
+
+---
+
+## 🚀 Installation
+
+```bash
+conda create -n indicf5 python=3.10 -y
+conda activate indicf5
+pip install git+https://github.com/ai4bharat/IndicF5.git
+```
+
+## 🎙 Usage
+
+To generate speech, you need to provide **three inputs**:
+
+1. **Text to synthesize** – The content you want the model to speak.
+2. **A reference prompt audio** – An example speech clip that guides the model’s prosody and speaker characteristics.
+3. **Text spoken in the reference prompt audio** – The transcript of the reference prompt audio.
+
+```python
+from transformers import AutoModel
+import numpy as np
+import soundfile as sf
+
+# Load IndicF5 from Hugging Face
+repo_id = "ai4bharat/IndicF5"
+model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)
+
+# Generate speech
+audio = model(
+    "नमस्ते! संगीत की तरह जीवन भी खूबसूरत होता है, बस इसे सही ताल में जीना आना चाहिए.",
+    ref_audio_path="prompts/PAN_F_HAPPY_00001.wav",
+    ref_text="ਭਹੰਪੀ ਵਿੱਚ ਸਮਾਰਕਾਂ ਦੇ ਭਵਨ ਨਿਰਮਾਣ ਕਲਾ ਦੇ ਵੇਰਵੇ ਗੁੰਝਲਦਾਰ ਅਤੇ ਹੈਰਾਨ ਕਰਨ ਵਾਲੇ ਹਨ, ਜੋ ਮੈਨੂੰ ਖੁਸ਼ ਕਰਦੇ  ਹਨ।"
+)
+
+# Normalize and save output
+if audio.dtype == np.int16:
+    audio = audio.astype(np.float32) / 32768.0
+sf.write("namaste.wav", np.array(audio, dtype=np.float32), samplerate=24000)
+print("Audio saved succesfully.")
+```
+
+You can find example prompt audios used [here](https://huggingface.co/ai4bharat/IndicF5/tree/main/prompts).
+
+## Terms of Use
+
+By using this model, you agree to only clone voices for which you have explicit permission. Unauthorized voice cloning is strictly prohibited. Any misuse of this model is the responsibility of the user.
+
+## References
+
+We would like to extend our gratitude to the authors of **[F5-TTS](https://github.com/SWivid/F5-TTS)** for their invaluable contributions and inspiration to this work. Their efforts have played a crucial role in advancing the field of text-to-speech synthesis.
+
+## 📖 Citation
+
+If you use **IndicF5** in your research or projects, please consider citing it:
+
+### 🔹 BibTeX
+
+```bibtex
+@misc{AI4Bharat_IndicF5_2025,
+  author       = {Praveen S V and Srija Anand and Soma Siddhartha and Mitesh M. Khapra},
+  title        = {IndicF5: High-Quality Text-to-Speech for Indian Languages},
+  year         = {2025},
+  url          = {https://github.com/AI4Bharat/IndicF5},
+}
+```

checkpoints/vocab.txt

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+𠮶

config.json

@@ -0,0 +1,16 @@
+{
+  "architectures": [
+    "INF5Model"
+  ],
+  "auto_map": {
+    "AutoConfig": "model.INF5Config",
+    "AutoModel": "model.INF5Model"
+  },
+  "ckpt_path": "checkpoints/model_best.pt",
+  "model_type": "inf5",
+  "remove_sil": true,
+  "speed": 1.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "vocab_path": "checkpoints/vocab.txt"
+}

model.py

@@ -0,0 +1,165 @@
+import sys
+import os
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(current_dir)
+
+from transformers import PreTrainedModel, PretrainedConfig, AutoConfig
+import torch
+import numpy as np
+from f5_tts.infer.utils_infer import (
+    infer_process,
+    load_model,
+    load_vocoder,
+    preprocess_ref_audio_text,
+)
+from f5_tts.model import DiT
+import soundfile as sf
+import io
+from pydub import AudioSegment, silence
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+import os
+
+class INF5Config(PretrainedConfig):
+    model_type = "inf5"
+
+    def __init__(self, ckpt_path: str = "checkpoints/model_best.pt", vocab_path: str = "checkpoints/vocab.txt", 
+                 speed: float = 1.0, remove_sil: bool = True, **kwargs):
+        super().__init__(**kwargs)
+        self.ckpt_path = ckpt_path
+        self.vocab_path = vocab_path
+        self.speed = speed
+        self.remove_sil = remove_sil
+
+class INF5Model(PreTrainedModel):
+    config_class = INF5Config
+
+    def __init__(self, config):
+        super().__init__(config)
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        # Load vocoder
+        self.vocoder = torch.compile(load_vocoder(vocoder_name="vocos", is_local=False, device=device))
+
+        # Download and load model weights
+        # safetensors_path = hf_hub_download(config.name_or_path, filename="model.safetensors")
+        # print(f"Loading model weights from {safetensors_path} (safetensors)...")
+        # state_dict = load_file(safetensors_path, device=str(device))
+
+        # Download vocab.txt from HF Hub
+        vocab_path = hf_hub_download(config.name_or_path, filename="checkpoints/vocab.txt")
+
+        self.ema_model = torch.compile(load_model(
+                DiT,
+                dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4),
+                mel_spec_type="vocos",
+                vocab_file=vocab_path,
+                device=device
+            )
+        )
+
+        # # Load state dict into model
+        # self.ema_model.load_state_dict(state_dict, strict=False)
+
+    
+    def forward(self, text: str, ref_audio_path: str, ref_text: str):
+        """
+        Generate speech given a reference audio & text input.
+        
+        Args:
+            text (str): The text to be synthesized.
+            ref_audio_path (str): Path to the reference audio file.
+            ref_text (str): The reference text.
+
+        Returns:
+            np.array: Generated waveform.
+        """
+
+        if not os.path.exists(ref_audio_path):
+            raise FileNotFoundError(f"Reference audio file {ref_audio_path} not found.")
+
+        # Load reference audio & text
+        ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_path, ref_text)
+
+        
+        self.ema_model.to(self.device)
+        self.vocoder.to(self.device)
+        
+        # Perform inference
+        audio, final_sample_rate, _ = infer_process(
+            ref_audio,
+            ref_text,
+            text,
+            self.ema_model,
+            self.vocoder,
+            mel_spec_type="vocos",
+            speed=self.config.speed,
+            device=self.device,
+        )
+
+        # Convert to pydub format and remove silence if needed
+        buffer = io.BytesIO()
+        sf.write(buffer, audio, samplerate=24000, format="WAV")
+        buffer.seek(0)
+        audio_segment = AudioSegment.from_file(buffer, format="wav")
+
+        if self.config.remove_sil:
+            non_silent_segs = silence.split_on_silence(
+                audio_segment,
+                min_silence_len=1000,
+                silence_thresh=-50,
+                keep_silence=500,
+                seek_step=10,
+            )
+            non_silent_wave = sum(non_silent_segs, AudioSegment.silent(duration=0))
+            audio_segment = non_silent_wave
+
+        # Normalize loudness
+        target_dBFS = -20.0
+        change_in_dBFS = target_dBFS - audio_segment.dBFS
+        audio_segment = audio_segment.apply_gain(change_in_dBFS)
+
+        return np.array(audio_segment.get_array_of_samples())
+
+
+
+if __name__ == '__main__':
+    model = INF5Model(INF5Config(ckpt_path="checkpoints/model_best.pt", vocab_path="checkpoints/vocab.txt"))
+    model.save_pretrained("INF5")
+    model.config.save_pretrained("INF5")
+
+    import numpy as np
+    import soundfile as sf
+    from transformers import AutoConfig, AutoModel
+    
+    AutoConfig.register("inf5", INF5Config)
+    AutoModel.register(INF5Config, INF5Model)
+
+    model = AutoModel.from_pretrained("INF5")
+    audio = model("नमस्ते! संगीत की तरह जीवन भी खूबसूरत होता है, बस इसे सही ताल में जीना आना चाहिए.", 
+                  ref_audio_path="prompts/PAN_F_HAPPY_00001.wav",
+                  ref_text="भਹੰਪੀ ਵਿੱਚ ਸਮਾਰਕਾਂ ਦੇ ਭਵਨ ਨਿਰਮਾਣ ���ਲਾ ਦੇ ਵੇਰਵੇ ਗੁੰਝਲਦਾਰ ਅਤੇ ਹੈਰਾਨ ਕਰਨ ਵਾਲੇ ਹਨ, ਜੋ ਮੈਨੂੰ ਖੁਸ਼ ਕਰਦੇ  ਹਨ।")
+    
+    if audio.dtype == np.int16:
+        audio = audio.astype(np.float32) / 32768.0  
+    sf.write("samples/namaste.wav", np.array(audio, dtype=np.float32), samplerate=24000)
+
+    from huggingface_hub import HfApi
+
+    repo_id = "svp19/INF5"  # Change to your HF repo
+
+    # Upload model directory to HF
+    api = HfApi()
+    api.upload_folder(
+        folder_path="INF5",
+        repo_id=repo_id,
+        repo_type="model"
+    )
+    print(f"Model pushed to https://huggingface.co/{repo_id} 🚀")
+
+    print("Verify Upload")
+    from transformers import AutoModel
+    model = AutoModel.from_pretrained(repo_id)
+    print("Success")
+