Create app.py

app.py

@@ -0,0 +1,79 @@
+import whisper
+import datetime
+import subprocess
+import torch
+import pyannote.audio
+from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
+embedding_model = PretrainedSpeakerEmbedding( 
+    "speechbrain/spkrec-ecapa-voxceleb",
+    device=torch.device("cuda"))
+
+from pyannote.audio import Audio
+from pyannote.core import Segment
+
+import wave
+import contextlib
+
+from sklearn.cluster import AgglomerativeClustering
+import numpy as np
+
+
+num_speakers = 2 
+
+language = 'English' 
+
+model_size = 'medium'
+model = whisper.load_model(model_size)
+
+
+model_name = model_size
+audio = Audio()
+def segmentembedding(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])
+def time(secs):
+  return datetime.timedelta(seconds=round(secs))
+from transformers import pipeline
+summarizer = pipeline("summarization", model="kabita-choudhary/finetuned-bart-for-conversation-summary")
+
+def translatetotext(path):
+    out=""
+    if path[-3:] != 'wav':
+      subprocess.call(['ffmpeg', '-i', path, 'audio.wav', '-y'])
+      path = 'audio.wav'
+    result = model.transcribe(path)
+    segments = result["segments"]
+    print(segments)
+    with contextlib.closing(wave.open(path,'r')) as f:
+      frames = f.getnframes()
+      rate = f.getframerate()
+      duration = frames / float(rate)
+    f.close()
+    embeddings = np.zeros(shape=(len(segments), 192))
+    for i, segment in enumerate(segments):
+      embeddings[i] = segment_embedding(segment)
+    embeddings = np.nan_to_num(embeddings)
+    clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
+    labels = clustering.labels_
+    for i in range(len(segments)):
+      segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
+    f = open("transcript.txt", "w") 
+    for (i, segment) in enumerate(segments):
+      if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]:
+        f.write("\n" + segment["speaker"] + ' ' + str(time(segment["start"])) + '\n')
+        out=out+segment["speaker"]
+      f.write(segment["text"][1:] + ' ')
+      out=out+segment["text"][1:] + '\n'
+    f.close()
+    summary = summarizer(out)
+    return out,summary
+
+demo = gr.Interface(
+    fn=translatetotext,
+    inputs=gr.Audio(source="upload",type="filepath"),
+    outputs=["text","text"]
+)
+demo.launch(debug=True)