Update app.py

app.py

@@ -1,15 +1,29 @@
 import torch
+import torchaudio
+from torchaudio.transforms import Resample
 from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
 from audio_recorder_streamlit import audio_recorder
-import numpy as np
 import streamlit as st
 
+def preprocess_audio(audio_bytes, sample_rate=16000):
+    # Load audio and convert to mono if necessary
+    waveform, _ = torchaudio.load(audio_bytes, normalize=True)
+    if waveform.size(0) > 1:
+        waveform = torch.mean(waveform, dim=0, keepdim=True)
+    
+    # Resample if needed
+    if waveform.shape[1] != sample_rate:
+        resampler = Resample(orig_freq=waveform.shape[1], new_freq=sample_rate)
+        waveform = resampler(waveform)
+    
+    return waveform
+
 def transcribe_audio(audio_bytes):
     model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-mustc-en-fr-st")
     processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-mustc-en-fr-st")
 
-    # Convert audio bytes to tensors
-    input_features = torch.tensor(audio_bytes).unsqueeze(0)  # Assuming audio_bytes is numpy array
+    # Preprocess audio
+    input_features = preprocess_audio(audio_bytes)
 
     # Generate transcription
     generated_ids = model.generate(input_features)
@@ -18,7 +32,7 @@ def transcribe_audio(audio_bytes):
     return translation
 
 st.title("Audio to Text Transcription..")
-audio_bytes = audio_recorder(pause_threshold=3.0, sample_rate=16_000)
+audio_bytes = audio_recorder(pause_threshold=3.0, sample_rate=16000)
 if audio_bytes:
     st.audio(audio_bytes, format="audio/wav")