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| import torch | |
| import torchaudio | |
| import numpy as np | |
| from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline | |
| from pydub import AudioSegment | |
| import os | |
| import gradio as gr | |
| # Load the model and processor | |
| model_id = "hackergeek98/whisper-fa-tinyyy" | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id).to(device) | |
| processor = AutoProcessor.from_pretrained(model_id) | |
| # Create ASR pipeline | |
| pipe = pipeline( | |
| "automatic-speech-recognition", | |
| model=model, | |
| tokenizer=processor.tokenizer, | |
| feature_extractor=processor.feature_extractor, | |
| device=0 if torch.cuda.is_available() else -1, | |
| ) | |
| # Convert audio to WAV format | |
| def convert_to_wav(audio_path): | |
| audio = AudioSegment.from_file(audio_path) | |
| audio = audio.set_channels(1) # Ensure mono audio | |
| wav_path = "converted_audio.wav" | |
| audio.export(wav_path, format="wav") | |
| return wav_path | |
| # Split long audio into chunks | |
| def split_audio(audio_path, chunk_length_ms=30000): # Default: 30 sec per chunk | |
| audio = AudioSegment.from_wav(audio_path) | |
| chunks = [audio[i:i+chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)] | |
| chunk_paths = [] | |
| for i, chunk in enumerate(chunks): | |
| chunk_path = f"chunk_{i}.wav" | |
| chunk.export(chunk_path, format="wav") | |
| chunk_paths.append(chunk_path) | |
| return chunk_paths | |
| # **🔹 Fixed: Convert Stereo to Mono Before Processing** | |
| def transcribe_audio_chunk(chunk_path): | |
| waveform, sampling_rate = torchaudio.load(chunk_path) # Load audio | |
| if waveform.shape[0] > 1: # If stereo (more than 1 channel) | |
| waveform = torch.mean(waveform, dim=0, keepdim=True) # Convert to mono | |
| waveform = waveform.numpy() # Convert to numpy | |
| result = pipe({"raw": waveform, "sampling_rate": sampling_rate}) # Pass raw data | |
| return result["text"] | |
| # Transcribe a long audio file | |
| def transcribe_long_audio(audio_path): | |
| wav_path = convert_to_wav(audio_path) | |
| chunk_paths = split_audio(wav_path) | |
| transcription = "" | |
| for chunk in chunk_paths: | |
| transcription += transcribe_audio_chunk(chunk) + "\n" | |
| os.remove(chunk) # Remove processed chunk | |
| os.remove(wav_path) # Cleanup original file | |
| return transcription | |
| # Gradio interface | |
| def transcribe_interface(audio_file): | |
| if not audio_file: | |
| return "No file uploaded." | |
| return transcribe_long_audio(audio_file) | |
| iface = gr.Interface( | |
| fn=transcribe_interface, | |
| inputs=gr.Audio(type="filepath"), | |
| outputs="text", | |
| title="Whisper ASR - Transcription", | |
| description="Upload an audio file, and the model will transcribe it." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |