update app.py

app.py

@@ -8,15 +8,10 @@ asr = pipeline(
     feature_extractor="facebook/wav2vec2-large-960h-lv60-self",
     
 )
-pipeline1 = Pipeline.from_pretrained("pyannote/speaker-segmentation")
+speaker_segmentation = Pipeline.from_pretrained("pyannote/speaker-segmentation")
 
-def diarization(file_input,mic_input,selection):
-    mic_path = None if mic_input is None else mic_input.name
-    audio = file_input if selection == "Upload" else mic_path
-    if audio is None:
-        return "Please check your inputs!", ""
-
-    speaker_output = pipeline1(audio)
+def segmentation(audio):
+    speaker_output = speaker_segmentation(audio)
     text_output = asr(audio,return_timestamps="word")
     
     full_text = text_output['text'].lower()
@@ -26,29 +21,31 @@ def diarization(file_input,mic_input,selection):
     i = 0
     for turn, _, speaker in speaker_output.itertracks(yield_label=True):
         diarized = ""
-        while i < len(chunks):
-            time_index = chunks[i]['timestamp'][1]
-            if time_index >= turn.start and time_index <= turn.end:
-                diarized += chunks[i]['text'].lower() + ' '
-            if time_index >= turn.end: break
+        while i < len(chunks) and chunks[i]['timestamp'][1] <= turn.end:
+            diarized += chunks[i]['text'].lower() + ' '
             i += 1
-
-        diarized_output += "{} said '{}' from {:.3f} to {:.3f}\n".format(speaker,diarized,turn.start,turn.end)
+        
+        if diarized != "":
+            diarized_output += "{}: ''{}'' from {:.3f}-{:.3f}\n".format(speaker,diarized,turn.start,turn.end)
         
     return diarized_output, full_text
 
-title = "Speech Recognition with Speaker Diarization"
-description = "Speaker Diarization is the act of attributing parts of the audio recording to different speakers. This space aims to distinguish the speakers and apply speech-to-text from a given input audio file. Pre-trained models from Pyannote[1] for the Speaker Diarization and [2]."
-article = "<p style='text-align: center'><a href='https://github.com/pyannote/pyannote-audio' target='_blank'>[1] Pyannote - Speaker Diarization model</a></p>"
-inputs = [gr.inputs.Audio(source="upload", type="filepath", label="Upload your audio file here:", optional=True),
-        gr.inputs.Audio(source="microphone", type="file",label="Or use your Microphone:", optional=True),
-        gr.inputs.Radio(["Upload","Microphone"], type="value", label="Select which input:")]
+title = "Speech Recognition with Speaker Segmentation"
+description = "Speaker Diarization is the act of attributing individual speakers to their corresponding parts in an audio recording. This space aims to distinguish the speakers with speaker segmentation and their speech with speech-to-text from a given input audio file. Pre-trained models used are Pyannote[1] for the Speaker Segmentation and Wav2Vec2[2] for the Automatic Speech Recognition."
+article = "<p style='text-align: center'><a href='https://github.com/pyannote/pyannote-audio' target='_blank'>[1] Pyannote - Speaker Segmentation model (GitHub repo)</a></p>"
+article += "<p style='text-align: center'><a href='https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20' target='_blank'>[2] Facebook Wav2Vec2 (GitHub repo)</a></p>"
+article += "<p style='text-align: center'>Audio File Sources: <a href='https://www.youtube.com/watch?v=DYu_bGbZiiQ&t=132s' target='_blank'>1</a> <a href='https://www.youtube.com/watch?v=DDjWTWHHkpk&t=29s' target='_blank'>2</a> <a href='https://www.youtube.com/watch?v=G2xWg2ckKHI&t=24s' target='_blank'>3</a> <a href='https://www.youtube.com/watch?v=sCcv9uqSBU0&t=32s' target='_blank'>4</a> <a href='https://www.youtube.com/watch?v=K1hlp0DCE_8&t=71s' target='_blank'>5</a></p>"
+
+inputs = gr.inputs.Audio(source="upload", type="filepath", label="Upload your audio file here:")
 outputs = [gr.outputs.Textbox(type="auto", label="Diarized Output"),
-        gr.outputs.Textbox(type="auto",label="Full ASR Text for comparison")]
-examples = [["test_audio1.wav",None,"Upload"],
-            ["test_audio2.wav",None,"Upload"]]
+            gr.outputs.Textbox(type="auto",label="Full ASR Text for comparison")]
+examples = [["meeting_audio.wav"],
+            ["noisy_london_interview.wav"],
+            ["clean_london_interview.wav"],
+            ["podcast_audio.wav"],
+            ["air_traffic_control_audio.wav"],]
 
-app = gr.Interface(fn=diarization,
+app = gr.Interface(fn=segmentation,
                 inputs=inputs,
                 outputs=outputs,
                 examples=examples,