app.py

"""Røst ASR demo."""

import os
import warnings

import gradio as gr
import numpy as np
import samplerate
import torch
from punctfix import PunctFixer
from transformers import pipeline

warnings.filterwarnings("ignore", category=FutureWarning)

TITLE = "Røst ASR Demo"
DESCRIPTION = """
This is a demo of the Danish speech recognition model
[Røst](https://huggingface.co/alexandrainst/roest-315m). Speak into the microphone and
see the text appear on the screen!
"""

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
transcriber = pipeline(
    task="automatic-speech-recognition",
    model="alexandrainst/roest-315m",
    device=device,
    token=os.environ["HUGGINGFACE_HUB_TOKEN"],
)
transcription_fixer = PunctFixer(language="da", device=device)

def transcribe_audio(sampling_rate_and_audio: tuple[int, np.ndarray]) -> str:
    """Transcribe the audio.

    Args:
        sampling_rate_and_audio:
            A tuple with the sampling rate and the audio.

    Returns:
        The transcription.
    """
    sampling_rate, audio = sampling_rate_and_audio
    if audio.ndim > 1:
        audio = np.mean(audio, axis=1)
    audio = samplerate.resample(audio, 16_000 / sampling_rate, "sinc_best")
    transcription = transcriber(inputs=audio)
    if not isinstance(transcription, dict):
        return ""
    cleaned_transcription = transcription_fixer.punctuate(
        text=transcription["text"]
    )
    return cleaned_transcription

demo = gr.Interface(
    fn=transcribe_audio,
    inputs=gr.Audio(sources=["microphone", "upload"]),
    outputs="textbox",
    title=TITLE,
    description=DESCRIPTION,
    allow_flagging="never",
)

demo.launch()