Update app.py

app.py

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+from google.colab import files
+uploaded = files.upload()
+path = next(iter(uploaded))
+
+num_speakers = 2 #@param {type:"integer"}
+
+language = 'English' #@param ['any', 'English']
+
+model_size = 'large' #@param ['tiny', 'base', 'small', 'medium', 'large']
+
+
+model_name = model_size
+if language == 'English' and model_size != 'tiny':
+  model_name += '.en'
+
+!pip install -q git+https://github.com/openai/whisper.git > /dev/null
+!pip install -q git+https://github.com/pyannote/pyannote-audio > /dev/null
+
+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
+
+
+if path[-3:] != 'wav':
+  subprocess.call(['ffmpeg', '-i', path, 'audio.wav', '-y'])
+  path = 'audio.wav'
+
+model = whisper.load_model(model_size)
+
+result = model.transcribe(path)
+segments = result["segments"]
+
+with contextlib.closing(wave.open(path,'r')) as f:
+  frames = f.getnframes()
+  rate = f.getframerate()
+  duration = frames / float(rate)
+
+audio = Audio()
+
+def segment_embedding(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])
+
+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)
+  # speaker = 'Held'
+  # speaker = 'Heldisha'
+  # if segments[i]["speaker"]== 'SPEAKER 1':
+  #   segments[i]["speaker"] = 'Held'
+  # elif segments[i]["speaker"]== 'SPEAKER 2':
+  #   segments[i]["speaker"] = 'Heldisha'
+  # if segments[i]["speaker"]== 'SPEAKER 1':
+    # segments[i]["speaker"] = segments.index('n')
+# k = list(segments)
+# print(k[5])
+
+def time(secs):
+  return datetime.timedelta(seconds=round(secs))
+
+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')
+  f.write(segment["text"][1:] + ' ')
+f.close()
+
+# with open('transcript.txt', 'r') as file:
+#     text = file.read()
+#     words = text.split()
+#     i = words.index('name')
+#     if (words[i-1] == 'My') or (words[i-1] == 'my') and (words[i+1] == 'is'):
+#         name1 = words[i+2]
+#         print(name1)
+  
+#     with open('transcript.txt', 'r') as file:
+#         text = file.read()
+#         new_text = text.replace('SPEAKER 1', name1)
+#     with open('transcript.txt', 'w') as file:
+#         file.write(new_text)
+
+
+
+# with open('transcript.txt', 'r') as file:
+    
+#     text = file.read()
+#     words = text.split()
+#     i = words.index('name')
+#     if (words[i+3] == 'What') or (1<2) and (words[i+1] == 'is') or 1<2:
+#         name2 = words[i+22]
+#         print(name2)
+#     with open('transcript.txt', 'r') as file:
+#         text = file.read()
+#         new_text = text.replace('SPEAKER 2', name2)
+#     with open('transcript.txt', 'w') as file:
+#         file.write(new_text)
+    
+    
+    
+
+
+
+    
+    
+