Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC | |
| import torch | |
| # Load the model and processor for Wav2Vec 2.0 | |
| model_id = "facebook/wav2vec2-base-960h" | |
| processor = Wav2Vec2Processor.from_pretrained(model_id) | |
| model = Wav2Vec2ForCTC.from_pretrained(model_id) | |
| model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) | |
| def transcribe(file_path): | |
| try: | |
| audio_input, sampling_rate = processor.audio_file_to_array(file_path) | |
| input_values = processor(audio_input, sampling_rate=sampling_rate, return_tensors="pt").input_values | |
| input_values = input_values.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) | |
| with torch.no_grad(): | |
| logits = model(input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = processor.batch_decode(predicted_ids)[0] | |
| return transcription | |
| except Exception as e: | |
| print(f"Error during transcription: {e}") | |
| return "Transcription error" | |
| # Gradio interface setup | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Transcribe Audio"): | |
| with gr.Row(): | |
| audio_input = gr.Audio(label="Upload audio file or record", type="filepath") | |
| with gr.Row(): | |
| audio_output = gr.Textbox(label="Transcription") | |
| audio_input.change(transcribe, inputs=audio_input, outputs=audio_output) | |
| demo.launch() | |