app.py

import gradio as gr
from transformers import pipeline
import numpy as np
import librosa
from punctuators.models import PunctCapSegModelONNX

transcriber = pipeline("automatic-speech-recognition", model="Oysiyl/w2v-bert-2.0-ukrainian-colab-CV16.0")
punct_cap_model = PunctCapSegModelONNX.from_pretrained("1-800-BAD-CODE/xlm-roberta_punctuation_fullstop_truecase")

def transcribe(audio):
    sr, y = audio
    y = y.astype(np.float32)
    y /= np.max(np.abs(y))
    if sr != 16000:
        y = librosa.resample(y, orig_sr=sr, target_sr=16000)
    transcribed_text = transcriber({"sampling_rate": 16000, "raw": y})["text"]
    punct_cap_text = punct_cap_model.infer(texts=[transcribed_text], apply_sbd=True)[0][0]
    return punct_cap_text


demo = gr.Interface(
    transcribe,
    gr.Audio(sources=["upload", "microphone"]),
    outputs="text",
    title="Automatic Speech Recognition for Ukrainian language demo",
    description="Click on the example below, upload audio from file or say something in microphone!",
    examples=[["examples/asr_example.wav"], ["examples/tts_example.wav"]],
    cache_examples=True
)

demo.launch()