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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) |