Add application file

app.py

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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']
+
+    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)

requirements.txt

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+torch
+numpy
+transformers
+soundfile