add transcribe

app.py

@@ -1,7 +1,109 @@
+import whisper
 import gradio as gr
+import datetime
 
-def greet(name):
-    return "Hello " + name + "!!"
+import subprocess
+import wave
+import contextlib
+
+import torch
+import pyannote.audio
+from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
+from pyannote.audio import Audio
+from pyannote.core import Segment
+from sklearn.cluster import AgglomerativeClustering
+import numpy as np
+
+model = whisper.load_model("large-v2")
+embedding_model = PretrainedSpeakerEmbedding( 
+    "speechbrain/spkrec-ecapa-voxceleb",
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+)
+
+def transcribe(audio, num_speakers):
+  path, error = convert_to_wav(audio)
+  if error is not None:
+    return error
+
+  duration = get_duration(path)
+  if duration > 4 * 60 * 60:
+    return "Audio duration too long"
+
+  result = model.transcribe(path)
+  segments = result["segments"]
+
+  num_speakers = min(max(round(num_speakers), 1), len(segments))
+  if len(segments) == 1:
+    segments[0]['speaker'] = 'SPEAKER 1'
+  else:
+    embeddings = make_embeddings(path, segments, duration)
+    add_speaker_labels(segments, embeddings, num_speakers)
+  output = get_output(segments)
+  return output
+
+def convert_to_wav(path):
+  if path[-3:] != 'wav':
+    new_path = '.'.join(path.split('.')[:-1]) + '.wav'
+    try:
+      subprocess.call(['ffmpeg', '-i', path, new_path, '-y'])
+    except:
+      return path, 'Error: Could not convert file to .wav'
+    path = new_path
+  return path, None
+
+def get_duration(path):
+  with contextlib.closing(wave.open(path,'r')) as f:
+    frames = f.getnframes()
+    rate = f.getframerate()
+    return frames / float(rate)
+
+def make_embeddings(path, segments, duration):
+  embeddings = np.zeros(shape=(len(segments), 192))
+  for i, segment in enumerate(segments):
+    embeddings[i] = segment_embedding(path, segment, duration)
+  return np.nan_to_num(embeddings)
+
+audio = Audio()
+
+def segment_embedding(path, segment, duration):
+  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])
+
+def add_speaker_labels(segments, embeddings, num_speakers):
+  clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
+  labels = clustering.labels_
+  for i in range(len(segments)):
+    segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
+
+def time(secs):
+  return datetime.timedelta(seconds=round(secs))
+
+def get_output(segments):
+  output = ''
+  for (i, segment) in enumerate(segments):
+    if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]:
+      if i != 0:
+        output += '\n\n'
+      output += segment["speaker"] + ' ' + str(time(segment["start"])) + '\n\n'
+    output += segment["text"][1:] + ' '
+  return output
+
+
+iface = gr.Interface(
+    title = 'Whisper with Speaker Recognition', 
+    fn=transcribe, 
+    inputs=[
+        gr.inputs.Audio(source="upload", type="filepath"),
+        gr.inputs.Number(default=2, label="Number of Speakers")
+
+    ],
+    outputs=[
+        gr.outputs.Textbox(label='Transcript')
+    ]
+  ).launch()
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 iface.launch()