Update app.py

app.py

@@ -1,6 +1,8 @@
 import gradio as gr
 import torchaudio
 from transformers import pipeline
+import soundfile as sf
+import torch 
 
 # Load only the Moul-Sout-100 model
 asr_pipeline = pipeline("automatic-speech-recognition", model="01Yassine/moulsot_v0.2_1000")
@@ -10,31 +12,76 @@ asr_pipeline.model.generation_config.input_ids = asr_pipeline.model.generation_c
 asr_pipeline.model.generation_config.forced_decoder_ids = None
 
 
+def load_audio(audio_path):
+    """Robustly load any audio file into (waveform, sr)"""
+    try:
+        waveform, sr = torchaudio.load(audio_path)
+    except Exception:
+        # fallback for unknown backends
+        data, sr = sf.read(audio_path)
+        waveform = torch.tensor(data, dtype=torch.float32).T
+        if waveform.ndim == 1:
+            waveform = waveform.unsqueeze(0)
+    return waveform, sr
+
+
 def ensure_mono_16k(audio_path):
-    """Load audio, convert to mono + 16kHz, and save a temp version."""
-    waveform, sr = torchaudio.load(audio_path)
-    
-    # Convert to mono if necessary
+    """Convert audio to mono + 16 kHz"""
+    waveform, sr = load_audio(audio_path)
     if waveform.shape[0] > 1:
         waveform = waveform.mean(dim=0, keepdim=True)
-    
-    # Resample to 16kHz if necessary
     if sr != 16000:
         resampler = torchaudio.transforms.Resample(sr, 16000)
         waveform = resampler(waveform)
         sr = 16000
-    
-    tmp_path = "/tmp/processed_16k.wav"
+    return waveform, sr
+
+
+def trim_leading_silence(waveform, sr, keep_ms=100, threshold=0.01):
+    """Trim leading silence, keep ≤ keep_ms ms"""
+    energy = waveform.abs().mean(dim=0)
+    non_silence_idx = (energy > threshold).nonzero(as_tuple=True)[0]
+    if len(non_silence_idx) == 0:
+        return waveform  # all silence
+    first_non_silence = non_silence_idx[0].item()
+    keep_samples = int(sr * (keep_ms / 1000.0))
+    start = max(0, first_non_silence - keep_samples)
+    return waveform[:, start:]
+
+
+def preprocess_audio(audio_path):
+    waveform, sr = ensure_mono_16k(audio_path)
+    waveform = trim_leading_silence(waveform, sr, keep_ms=100, threshold=0.01)
+    tmp_path = "/tmp/processed_trimmed.wav"
     torchaudio.save(tmp_path, waveform, sr)
     return tmp_path
 
 
+# def ensure_mono_16k(audio_path):
+#     """Load audio, convert to mono + 16kHz, and save a temp version."""
+#     waveform, sr = torchaudio.load(audio_path)
+    
+#     # Convert to mono if necessary
+#     if waveform.shape[0] > 1:
+#         waveform = waveform.mean(dim=0, keepdim=True)
+    
+#     # Resample to 16kHz if necessary
+#     if sr != 16000:
+#         resampler = torchaudio.transforms.Resample(sr, 16000)
+#         waveform = resampler(waveform)
+#         sr = 16000
+    
+#     tmp_path = "/tmp/processed_16k.wav"
+#     torchaudio.save(tmp_path, waveform, sr)
+#     return tmp_path
+
+
 def transcribe(audio):
     if audio is None:
         return "Please record or upload an audio file."
     
     # Process and transcribe
-    processed_audio = ensure_mono_16k(audio)
+    processed_audio = preprocess_audio(audio)
     result = asr_pipeline(processed_audio)["text"]
     
     return result