Update app.py

app.py

@@ -3,6 +3,7 @@ import whisper
 import datetime
 import torch
 import subprocess
+import os
 from pyannote.audio import Audio
 from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
 from pyannote.core import Segment
@@ -19,52 +20,55 @@ audio = Audio()
 embedding_model = PretrainedSpeakerEmbedding("speechbrain/spkrec-ecapa-voxceleb", device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
 
 def transcribe_and_diarize(audio_file, num_speakers=2):
-    path = audio_file.name
-    # Convert to WAV if necessary
-    if path[-3:] != 'wav':
-        subprocess.call(['ffmpeg', '-i', path, 'audio.wav', '-y'])
-        path = 'audio.wav'
+    try:
+        path = audio_file.name
+        # Convert to WAV if necessary
+        if not path.endswith('.wav'):
+            subprocess.call(['ffmpeg', '-i', path, 'audio.wav', '-y'])
+            path = 'audio.wav'
 
-    # Transcribe audio
-    result = model.transcribe(path)
-    segments = result["segments"]
+        # Transcribe audio
+        result = model.transcribe(path)
+        segments = result["segments"]
 
-    # Get audio duration
-    with contextlib.closing(wave.open(path, 'r')) as f:
-        frames = f.getnframes()
-        rate = f.getframerate()
-        duration = frames / float(rate)
+        # Get audio duration
+        with contextlib.closing(wave.open(path, 'r')) as f:
+            frames = f.getnframes()
+            rate = f.getframerate()
+            duration = frames / float(rate)
 
-    # Define function to extract segment embeddings
-    def segment_embedding(segment):
-        start = segment["start"]
-        end = min(duration, segment["end"])
-        clip = Segment(start, end)
-        waveform, sample_rate = audio.crop(path, clip)
-        return embedding_model(waveform[None])
+        # Define function to extract segment embeddings
+        def segment_embedding(segment):
+            start = segment["start"]
+            end = min(duration, segment["end"])
+            clip = Segment(start, end)
+            waveform, sample_rate = audio.crop(path, clip)
+            return embedding_model(waveform[None])
 
-    # Extract embeddings for each segment
-    embeddings = np.zeros(shape=(len(segments), 192))
-    for i, segment in enumerate(segments):
-        embeddings[i] = segment_embedding(segment)
+        # Extract embeddings for each segment
+        embeddings = np.zeros(shape=(len(segments), 192))
+        for i, segment in enumerate(segments):
+            embeddings[i] = segment_embedding(segment)
 
-    embeddings = np.nan_to_num(embeddings)
+        embeddings = np.nan_to_num(embeddings)
 
-    # Perform speaker clustering
-    clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
-    labels = clustering.labels_
-    for i in range(len(segments)):
-        segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
+        # Perform speaker clustering
+        clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
+        labels = clustering.labels_
+        for i in range(len(segments)):
+            segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
 
-    # Generate transcript
-    transcript = ""
-    for i, segment in enumerate(segments):
-        if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]:
-            transcript += "\n" + segment["speaker"] + ' ' + str(datetime.timedelta(seconds=round(segment["start"]))) + '\n'
-        transcript += segment["text"][1:] + ' '
-        transcript += "\n\n"
-    
-    return transcript
+        # Generate transcript
+        transcript = ""
+        for i, segment in enumerate(segments):
+            if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]:
+                transcript += "\n" + segment["speaker"] + ' ' + str(datetime.timedelta(seconds=round(segment["start"]))) + '\n'
+            transcript += segment["text"][1:] + ' '
+            transcript += "\n\n"
+        
+        return transcript
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
 
 iface = gr.Interface(
     fn=transcribe_and_diarize,
@@ -77,4 +81,4 @@ iface = gr.Interface(
     description="Upload an audio file to get a transcription with speaker diarization."
 )
 
-iface.launch()
+iface.launch()