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import gradio as gr | |
import librosa | |
import soundfile as sf | |
import torch | |
from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC | |
#load wav2vec2 tokenizer and model | |
# define speech-to-text function | |
def asr_transcript(audio_file, language): | |
if language == "English": | |
model_name = "facebook/wav2vec2-large-960h-lv60-self" | |
elif language == "Russian": | |
model_name = "jonatasgrosman/wav2vec2-large-xlsr-53-russian" | |
elif language == "French": | |
model_name = "jonatasgrosman/wav2vec2-large-xlsr-53-french" | |
tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name) | |
model = Wav2Vec2ForCTC.from_pretrained(model_name) | |
transcript = "" | |
# Stream over 20 seconds chunks | |
stream = librosa.stream( | |
audio_file.name, block_length=20, frame_length=16000, hop_length=16000 | |
) | |
for speech in stream: | |
if len(speech.shape) > 1: | |
speech = speech[:, 0] + speech[:, 1] | |
input_values = tokenizer(speech, return_tensors="pt").input_values | |
logits = model(input_values).logits | |
predicted_ids = torch.argmax(logits, dim=-1) | |
transcription = tokenizer.batch_decode(predicted_ids)[0] | |
transcript += transcription.lower() + " " | |
return transcript | |
gradio_ui = gr.Interface( | |
fn=asr_transcript, | |
title="Speech-to-Text with HuggingFace+Wav2Vec2", | |
description="Upload an audio clip in Russian, English, or French and let AI do the hard work of transcribing", | |
inputs = [gr.inputs.Audio(label="Upload Audio File", type="file"), | |
gr.inputs.Radio(label="Pick an STT Model - (language)", | |
choices=["English", | |
"Russian", | |
"French"])], | |
outputs=gr.outputs.Textbox(label="Auto-Transcript"), | |
) | |
gradio_ui.launch() |