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| import os | |
| import gradio as gr | |
| import torch | |
| from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq | |
| import numpy as np | |
| import librosa | |
| import os | |
| from huggingface_hub import login | |
| hf_token = os.getenv("token") | |
| if hf_token is None: | |
| raise ValueError("HF_TOKEN environment variable not set.") | |
| login(token=hf_token) | |
| # Load model | |
| processor = AutoProcessor.from_pretrained("ragunath-ravi/whisper-mini-ta") | |
| model = AutoModelForSpeechSeq2Seq.from_pretrained("ragunath-ravi/whisper-mini-ta") | |
| # Move model to GPU if available | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model = model.to(device) | |
| def transcribe_audio(audio): | |
| """ | |
| Transcribe audio using the fine-tuned Whisper model | |
| """ | |
| if audio is None: | |
| return "No audio provided" | |
| # Load audio | |
| if isinstance(audio, str): # Path to audio file | |
| audio_array, sampling_rate = librosa.load(audio, sr=16000) | |
| else: # Tuple of (sample_rate, audio_array) | |
| sampling_rate, audio_array = audio | |
| # Convert to float32 if not already in floating-point format | |
| if not np.issubdtype(audio_array.dtype, np.floating): | |
| audio_array = audio_array.astype(np.float32) | |
| # Normalize if needed (assuming int16 or int32 input) | |
| if np.max(np.abs(audio_array)) > 1.0: | |
| audio_array = audio_array / 32768.0 # Normalize for 16-bit audio | |
| if sampling_rate != 16000: | |
| audio_array = librosa.resample(audio_array, orig_sr=sampling_rate, target_sr=16000) | |
| sampling_rate = 16000 | |
| # Process through model | |
| inputs = processor(audio_array, sampling_rate=sampling_rate, return_tensors="pt").to(device) | |
| with torch.no_grad(): | |
| generated_ids = model.generate(input_features=inputs.input_features) | |
| # Decode | |
| transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| return transcription | |
| # Create Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Tamil Speech Transcription") | |
| gr.Markdown("This app transcribes Tamil speech using a fine-tuned Whisper model. Upload an audio file or record using the microphone.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| # Set max_length to 10 seconds explicitly (10 * 16000 = 160000 samples) | |
| audio_input = gr.Audio( | |
| sources=["microphone", "upload"], | |
| type="numpy", | |
| label="Audio Input (Record up to 10 seconds)", | |
| max_length=10 | |
| ) | |
| transcribe_btn = gr.Button("Transcribe") | |
| with gr.Column(): | |
| output_text = gr.Textbox(label="Transcription Output", lines=5) | |
| # Connect components | |
| transcribe_btn.click(fn=transcribe_audio, inputs=audio_input, outputs=output_text) | |
| audio_input.change(fn=transcribe_audio, inputs=audio_input, outputs=output_text) | |
| gr.Markdown("## Instructions") | |
| gr.Markdown("1. Record audio using the microphone or upload an audio file") | |
| gr.Markdown("2. Click 'Transcribe' to get the Tamil transcription") | |
| gr.Markdown("3. For best results, speak clearly in Tamil") | |
| # Launch the app | |
| if __name__ == "__main__": | |
| demo.launch(share=True) |