Update app.py

app.py

@@ -16,63 +16,71 @@ import gradio as gr
 
 from sklearn.cluster import AgglomerativeClustering
 import numpy as np
+import os 
+from transformers import pipeline
+summarizer = pipeline("summarization", model="kabita-choudhary/finetuned-bart-for-conversation-summary")
 
 
-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'
+
+def translatetotext(vpath,no_of_speaker):
+    num_speakers = no_of_speaker 
+    language = 'English' 
+    model_size = 'small'
+    model = whisper.load_model(model_size)
+    model_name = model_size
+    _,file_ending = os.path.splitext(f'{vpath}')
+    print(f'file enging is {file_ending}')
+    path = vpath.replace(file_ending, ".wav")
+    print("starting conversion to wav")
+    os.system(f'ffmpeg -i "{vpath}" -ar 16000 -ac 1 -c:a pcm_s16le "{path}"')
     result = model.transcribe(path)
     segments = result["segments"]
     print(segments)
+    duration=0
     with contextlib.closing(wave.open(path,'r')) as f:
       frames = f.getnframes()
       rate = f.getframerate()
       duration = frames / float(rate)
+    def segment_embedding(segment):
+      audio = Audio()
+      start = segment["start"]
+            # Whisper overshoots the end timestamp in the last segment
+      end = min(duration, segment["end"])
+      clip = Segment(start, end)
+      waveform, sample_rate = audio.crop(path, clip)
+      return embedding_model(waveform[None])
     embeddings = np.zeros(shape=(len(segments), 192))
+    print(duration)
     for i, segment in enumerate(segments):
-      embeddings[i] = segmentembedding(segment)
+      embeddings[i] = segment_embedding(segment)
     embeddings = np.nan_to_num(embeddings)
     clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
     labels = clustering.labels_
+    print(labels)
     for i in range(len(segments)):
       segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
     f = open("transcript.txt", "w") 
+    out=""
     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:] + ' ')
+        f.write(segment["speaker"] + ' ' + str(time(segment["start"]))+' ' + str(time(segment["end"]))+' ')      
+        out=out+segment["speaker"]+' ' + str(time(segment["start"]))+' ' + str(time(segment["end"]))+' '
+      f.write(segment["text"][1:] + '\n')
       out=out+segment["text"][1:] + '\n'
     f.close()
+    
     summary = summarizer(out)
-    return out,summary
+    f = open("summary.txt", "w") 
+    f.write(summary[0]["summary_text"])
+    f.close()
+    return out,summary[0]["summary_text"],"transcript.txt","summary.txt"
 
 demo = gr.Interface(
     fn=translatetotext,
-    inputs=gr.Audio(source="upload",type="filepath"),
-    outputs=["text","text"]
+    inputs=[gr.Video(source="upload",type="filepath"),gr.Number(precision=0, value=2, label="Selected number of speakers", interactive=True)],
+    outputs=["text","text",gr.File(label="Download transcript"),gr.File(label="Download summary")]
 )
 demo.launch(debug=True)