app.py

import streamlit as st
from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM
import torch
import torchaudio
import torchaudio.functional as F


st.set_page_config(
    page_title="Swedish Speech-to-Text",
    page_icon="🎙️"
)
st.image(
    "https://emojipedia-us.s3.dualstack.us-west-1.amazonaws.com/thumbs/320/apple/325/studio-microphone_1f399-fe0f.png",
    width=100,
)
st.markdown("""
# Swedish high-quality transcription

Generate Swedish transcripts for download from an audio file with this high-quality speech-to-text model. The model is KBLab's wav2vec 2.0 large VoxRex Swedish (C) with a 4-gram language model, which you can access [here](https://huggingface.co/viktor-enzell/wav2vec2-large-voxrex-swedish-4gram).
""")

model_name = "viktor-enzell/wav2vec2-large-voxrex-swedish-4gram"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)


def run_inference(file):
    waveform, sample_rate = torchaudio.load(file)

    if sample_rate == 16_000:
        waveform = waveform[0]
    else:
        waveform = F.resample(waveform, sample_rate, 16_000)[0]

    inputs = processor(
        waveform,
        sampling_rate=16_000,
        return_tensors="pt",
        padding=True
    ).to(device)

    with torch.no_grad():
        logits = model(**inputs).logits

    return processor.batch_decode(logits.cpu().numpy()).text[0].lower()


uploaded_file = st.file_uploader("Choose a file", type=[".wav"])
if uploaded_file is not None:
    if uploaded_file.type != "audio/wav":
        pass
        # TODO: convert to wav
        # bytes = uploaded_file.getvalue()
        # audio_input = ffmpeg.input(bytes).audio
        # audio_output = ffmpeg.output(audio_input, "tmp.wav", format="wav")
        # ffmpeg.run(audio_output)

    transcript = run_inference(uploaded_file)

    st.download_button("Download transcript", transcript,
                       f"{uploaded_file.name}-swedish-transcript.txt")

    with st.expander("Transcript", expanded=True):
        st.write(transcript)