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import gradio as gr
import torch
import librosa
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Load Wav2Vec2 Model
MODEL_NAME = "facebook/wav2vec2-large-960h"
processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)
def transcribe(audio_file):
"""
Transcribes speech from an uploaded audio file or live microphone input.
"""
try:
# Load and convert audio to 16kHz
audio, rate = librosa.load(audio_file, sr=16000)
# Convert audio to tensor format for Wav2Vec
input_values = processor(audio, sampling_rate=16000, return_tensors="pt").input_values
# Run the model for transcription
with torch.no_grad():
logits = model(input_values).logits
# Convert predicted tokens into text
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
return transcription
except Exception as e:
return "Error processing file"
# UI Build
interface = gr.Interface(
fn=transcribe,
inputs=gr.Audio(sources=["microphone", "upload"], type="filepath", label="Speak or Upload Audio"),
outputs="text",
title="Wav2Vec2 Speech-to-Text Transcription",
description="Speak into your microphone or upload an audio file to get an automatic transcription.",
live=True # Real-time microphone processing
)
interface.launch(share=True) |