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| #import gradio as gr | |
| import tempfile | |
| from pydub import AudioSegment | |
| from transformers import pipeline | |
| from pyannote.audio import Pipeline | |
| import gradio as gr | |
| import gradio as gr | |
| from transformers import pipeline | |
| import whisper | |
| from pyannote.audio import Pipeline | |
| import tempfile | |
| import os | |
| def load_models(model_size): | |
| if model_size == "transcriber": | |
| model_name = "clinifyemr/yoruba-model-finetuned" | |
| transcriber = pipeline("automatic-speech-recognition", model=model_name) | |
| return transcriber, None | |
| else: | |
| model = whisper.load_model(model_size) | |
| return None, model | |
| from flask import jsonify | |
| import tempfile | |
| import os | |
| import io | |
| def process_audio(audio_file, num_speakers, model_size): | |
| transcriber, whisper_model = load_models(model_size) | |
| # Ensure audio file is provided | |
| if audio_file is None: | |
| return jsonify({"error": "Audio file is required"}), 400 | |
| try: | |
| audio_file.seek(0) # Reset the file pointer | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp: | |
| tmp.write(audio_file.read()) | |
| tmp_path = tmp.name | |
| # Initialize transcription_text | |
| transcription_text = None | |
| if transcriber: | |
| result = transcriber(tmp_path) | |
| transcription_text = result['text'] | |
| elif whisper_model: | |
| result = whisper_model.transcribe(tmp_path) | |
| transcription_text = result['text'] | |
| if transcription_text is None: | |
| raise ValueError("No transcription results") | |
| # Diarization process | |
| diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", use_auth_token=HF_TOKEN) | |
| diarization = diarization_pipeline(tmp_path, min_speakers=num_speakers, max_speakers=5) | |
| os.remove(tmp_path) # Cleanup the temporary file | |
| return jsonify({ | |
| "transcription": transcription_text, | |
| "diarization": diarization.get_timeline().json() | |
| }) | |
| except Exception as e: | |
| os.remove(tmp_path) # Ensure to cleanup on error | |
| return jsonify({"error": f"Error processing audio file: {e}"}), 500 | |
| def gradio_interface(audio_file, num_speakers, model_size): | |
| transcription, diarization = process_audio(audio_file, num_speakers, model_size) | |
| if transcription is None or diarization is None: | |
| return "Error in processing audio file", "No diarization result" | |
| return transcription, diarization | |
| iface = gr.Interface( | |
| fn=gradio_interface, | |
| inputs=[ | |
| gr.Audio(type="filepath", label="Upload Audio"), | |
| gr.Dropdown(choices=[1,2,3,4,5], label="Number of Speakers"), | |
| gr.Dropdown(choices=['base', 'small', 'medium', 'large', 'transcriber'], label="Model Selection") | |
| ], | |
| outputs=[ | |
| gr.Textbox(label="Transcription"), | |
| gr.JSON(label="Diarization Output") | |
| ], | |
| title="Audio Transcription and Speaker Diarization", | |
| description="Upload your audio file to transcribe and analyze speaker diarization." | |
| ) | |
| iface.launch() | |