File size: 1,204 Bytes
05d16fd 0b2172e 05d16fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 |
import gradio as gr
import torch
from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel
import soundfile as sf
# Load the model and processor
model = SpeechEncoderDecoderModel.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
# Define the transcription function
def transcribe_speech(file_info):
# Read the audio file
speech, _ = sf.read(file_info)
# Process the speech
inputs = processor(speech, sampling_rate=16_000, return_tensors="pt")
# Generate the transcription
generated_ids = model.generate(inputs=inputs["input_values"], attention_mask=inputs["attention_mask"])
# Decode the generated ids to text
transcription = processor.batch_decode(generated_ids)
return transcription[0]
# Create the Gradio interface
iface = gr.Interface(
fn=transcribe_speech,
inputs=gr.Audio(source="upload", type="filepath", label="Upload your MP3 file"),
outputs="text",
title="Speech to Text Conversion",
description="Upload an MP3 file to transcribe it to text using a state-of-the-art speech-to-text model."
)
# Run the Gradio app
iface.launch()
|