app.py

import streamlit as st
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torch
import numpy as np
import soundfile as sf
import io

st.title("Syllables per Second Calculator")
st.write("Upload an audio file to calculate the number of 'p', 't', and 'k' syllables per second.")

def get_syllables_per_second(audio_file):
    processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
    model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")

    audio_input, sample_rate = sf.read(io.BytesIO(audio_file.read()))

    if audio_input.ndim > 1 and audio_input.shape[1] == 2:
        audio_input = np.mean(audio_input, axis=1)

    input_values = processor(audio_input, return_tensors="pt").input_values

    with torch.no_grad():
        logits = model(input_values).logits
        predicted_ids = torch.argmax(logits, dim=-1)
        transcription = processor.batch_decode(predicted_ids, output_char_offsets=True)
        offsets = transcription['char_offsets']

    audio_duration = len(audio_input) / sample_rate
    syllable_count = sum(1 for item in offsets[0] if item['char'] in ['p', 't', 'k'])
    syllables_per_second = syllable_count / audio_duration

    return syllables_per_second

uploaded_file = st.file_uploader("Choose an audio file", type=["wav"])

if uploaded_file is not None:
    with st.spinner("Processing the audio file..."):
        result = get_syllables_per_second(uploaded_file)
        st.write("Syllables per second: ", result)