update app.py

app.py

@@ -1,17 +1,38 @@
 import gradio as gr
 from pyannote.audio import Pipeline
+from datasets import load_dataset
+from transformers import pipeline
+
+librispeech_en = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+asr = pipeline(
+    "automatic-speech-recognition",
+    model="facebook/s2t-wav2vec2-large-en-de",
+    feature_extractor="facebook/s2t-wav2vec2-large-en-de",
+)
+
+def speech_to_text(audio):
+    translation = asr(librispeech_en[0][audio])
+    return translation
 
 def diarization(audio):
     pipeline = Pipeline.from_pretrained("pyannote/speaker-segmentation")
-    #parameters = {"onset":0.7,"offset":0.3,"min_duration_on":0.0,"min_duration_off":0.0}
-    #pipeline.instantiate(parameters)
     output = pipeline(audio)
-    answer = ""
+    result = ""
     for turn, _, speaker in output.itertracks(yield_label=True):
-        answer += "{} said something starting from {:.2f} and ends on {:.2f}\n".format(speaker,turn.start,turn.end)
-    return answer
+        text_result = speech_to_text(audio)
+        result += "{} said '{}' from {:.3f} to {:.3f}\n".format(speaker,text_result,turn.start,turn.end)
+    return "No output" if result == "" else result
+
+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>"
 
 app = gr.Interface(fn=diarization,
-                inputs=gr.inputs.Audio(source="upload", type="filepath", label="audio"),
-                outputs="text")
-app.launch()
+                inputs=gr.inputs.Audio(source="upload", type="filepath", label="Upload your audio file here:"),
+                outputs=gr.outputs.Textbox(type="auto", label="OUTPUT"),
+                examples=[["test_audio1.wav"]],
+                title=title,
+                description=description,
+                article=article,
+                allow_flagging=False)
+app.launch(enable_queue=True)