Update app.py

app.py

@@ -1,3 +1,62 @@
-import gradio as gr
+#import gradio as gr
+import tempfile
+from pydub import AudioSegment
+from transformers import pipeline
+from pyannote.audio import Pipeline
+
+# Load models dynamically
+def load_models(model_size):
+    if model_size == "transcriber":
+        model_name = "clinifyemr/yoruba-model-finetuned"
+        transcriber = pipeline("automatic-speech-recognition", model=model_name)
+        return transcriber
+    else:
+        raise ValueError("Model size not supported in this application.")
+
+# Process the audio file
+def process_audio(file, num_speakers, model_size):
+    audio_file = AudioSegment.from_file(file.name)
+    transcriber = load_models(model_size)
+
+    # Temporary file setup
+    temp_path = tempfile.mktemp(suffix=".wav")
+    audio_file.export(temp_path, format="wav")
+
+    # Load diarization pipeline
+    diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", use_auth_token="HF_TOKEN")
+    diarization = diarization_pipeline(temp_path, min_speakers=num_speakers, max_speakers=5)
+
+    # Transcribe each segment
+    def transcribe_segment(start, end):
+        segment_audio = audio_file[start * 1000:end * 1000]  # pydub works in milliseconds
+        segment_path = tempfile.mktemp(suffix=".wav")
+        segment_audio.export(segment_path, format="wav")
+        transcription = transcriber(segment_path)
+        os.remove(segment_path)
+        return transcription['text']
+
+    transcripts = []
+    for segment, _, speaker in diarization.itertracks(yield_label=True):
+        transcription_text = transcribe_segment(segment.start, segment.end)
+        transcripts.append(f"Speaker {speaker}: {transcription_text}")
+
+    os.remove(temp_path)  # Clean up the temporary file
+    return "\n".join(transcripts)
+
+# Gradio interface setup
+iface = gr.Interface(
+    fn=process_audio,
+    inputs=[
+        #gr.components.Audio(label="Upload your audio file", type="file"),
+        gr.components.Audio(label="Upload your audio file"),
+        gr.components.Dropdown(choices=[1,2,3,4], label="Number of Speakers"),
+        gr.components.Dropdown(choices=['transcriber'], label="Model Selection")  # Assuming only 'transcriber' is relevant here
+    ],
+    outputs=gr.Textbox(label="Transcription"),
+    title="Audio Transcription and Speaker Diarization",
+    description="Upload your audio file to transcribe and analyze speaker diarization."
+)
+
+if __name__ == "__main__":
+    iface.launch()
 
-gr.load("models/DereAbdulhameed/updated_cli_2").launch(share=True)