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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']
# Find the start and end time offsets of the syllables
syllable_offsets = [item for item in offsets[0] if item['char'] in ['p', 't', 'k']]
if syllable_offsets: # if any syllable is found
first_syllable_offset = syllable_offsets[0]['start_offset'] / sample_rate
last_syllable_offset = syllable_offsets[-1]['end_offset'] / sample_rate
# Duration from the first to the last syllable
syllable_duration = last_syllable_offset - first_syllable_offset
else:
syllable_duration = 0
syllable_count = len(syllable_offsets)
syllables_per_second = syllable_count / syllable_duration if syllable_duration > 0 else 0
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)
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