Update app.py

app.py

@@ -1,18 +1,60 @@
 from transformers import pipeline
 import gradio as gr
+from pyannote.core import Annotation
+from pydub import AudioSegment
+import torchaudio
+from pyannote.audio import Pipeline
 
-pipe = pipeline(model="Hunzla/whisper-small-hi")  # change to "your-username/the-name-you-picked"
+diarization_pipe = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1",
+                                    use_auth_token="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")  # change to "your-username/the-name-you-picked"
 
-def transcribe(audio):
-    text = pipe(audio)["text"]
-    return text
+
+
+# Load the speech-to-text model (Whisper)
+asr_pipe = pipeline("automatic-speech-recognition", model="SyedAunZaidi/whisper-small-hi")
+
+def transcribe_with_diarization(audio_path):
+    # Get speaker segments using the diarization model
+  diarization_result = diarization_pipe(audio_path)
+
+    # Extract speaker segments and transcribe them using Whisper ASR
+  transcripts = []
+  for track, segment,speaker in diarization_result.itertracks(yield_label=True):
+      
+        print(segment)        
+        print(speaker)
+        
+        start_time = track.start
+        end_time = track.end
+        print(start_time)          
+        print(end_time)   
+        label = segment  # Extract the label manually
+        waveform, sample_rate = torchaudio.load("/content/drive/MyDrive/recording.mp3", normalize=True)
+        start_sample = int(start_time * sample_rate)
+        end_sample = int(end_time * sample_rate)
+        print(waveform)
+        interval_audio = waveform[:,start_sample:end_sample]
+            # Export the interval audio as a temporary WAV file
+        torchaudio.save("interval_audio.wav", interval_audio,sample_rate)
+        transcript = asr_pipe("interval_audio.wav")
+        print(transcript)        
+        start_time = segment.start
+        end_time = segment.end
+        label = track[0].label()  # Extract the label manually
+        speaker_audio = audio_path + f"[{start_time:.2f},{end_time:.2f}]"
+        transcript = asr_pipe(speaker_audio)[0]["text"]
+        transcripts.append(transcript)
+
+    # Combine the transcriptions from all speakers
+  text = " ".join(transcripts)
+  return text
 
 iface = gr.Interface(
-    fn=transcribe,
-    inputs=gr.Audio(source="microphone", type="filepath"),
+    fn=transcribe_with_diarization,
+    inputs=gr.Audio(source="microphone", type="filepath", filetype="wav"),
     outputs="text",
-    title="Whisper Small Hindi",
-    description="Realtime demo for Hindi speech recognition using a fine-tuned Whisper small model.",
+    title="Whisper Large Hindi with Speaker Diarization",
+    description="Real-time demo for Hindi speech recognition using a fine-tuned Whisper large model with speaker diarization.",
 )
 
-iface.launch()
+iface.launch()