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import gradio as gr | |
import os | |
import torch | |
import soundfile as sf | |
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, VitsModel, AutoTokenizer | |
# Ensure the output directory exists | |
os.makedirs("output_audio", exist_ok=True) | |
# Load the models and processors | |
asr_processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h") | |
asr_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h") | |
tts_model = VitsModel.from_pretrained("facebook/mms-tts-eng") | |
tts_tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng") | |
def speech_to_text(input_audio): | |
# Load and preprocess the audio | |
waveform, sr = sf.read(input_audio) | |
input_values = asr_processor(waveform, sampling_rate=sr, return_tensors="pt").input_values | |
# Perform speech recognition | |
with torch.no_grad(): | |
logits = asr_model(input_values).logits | |
predicted_ids = torch.argmax(logits, dim=-1) | |
# Decode the predicted IDs to text | |
transcription = asr_processor.batch_decode(predicted_ids)[0] | |
return transcription | |
def text_to_speech(text): | |
# Tokenize text and generate waveform | |
inputs = tts_tokenizer(text, return_tensors="pt") | |
with torch.no_grad(): | |
output = tts_model(**inputs).waveform | |
waveform = output.numpy() | |
# Define output path and save waveform as audio file | |
output_path = "output_audio/text_to_speech.wav" | |
sf.write(output_path, waveform.squeeze(), 22050) | |
return output_path | |
def speech_to_speech(input_audio, target_text): | |
# Synthesize speech directly from target text without transcribing the input audio | |
return text_to_speech(target_text) | |
iface = gr.Interface( | |
fn={ | |
"Speech to Text": speech_to_text, | |
"Text to Speech": text_to_speech, | |
"Speech to Speech": speech_to_speech | |
}, | |
inputs=[ | |
gr.Audio(label="Speech to Text"), | |
gr.Textbox(label="Text to Speech"), | |
[gr.Audio(label="Speech to Speech Input"), gr.Textbox(label="Target Text for Speech to Speech")] # Corrected: Use a list for multiple inputs | |
], | |
outputs=[ | |
gr.Textbox(label="Transcription"), | |
gr.Audio(label="Synthesized Speech"), | |
gr.Audio(label="Speech to Speech Output") | |
], | |
title="Speech Processing Application", | |
description="This app uses Facebook's Wav2Vec 2.0 for speech-to-text and VITS for text-to-speech.", | |
layout="vertical" | |
) | |
if __name__ == "__main__": | |
iface.launch() | |