app.py

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()