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# Install the assemblyai package by executing the command `pip3 install assemblyai` (macOS) or `pip install assemblyai` (Windows).
import io
import os
import time
from pyannote.audio import Pipeline
# Import the AssemblyAI module
from pprint import pprint
import torch
from transformers import pipeline
from transformers.utils import is_flash_attn_2_available
from pydub import AudioSegment
import numpy as np
from segment_wave_files import segment_wave_files
from transcribe_files import transcribe_segments
from transcript_analysis import transcript_analysis
#from huggingface_hub import login
#login()
hugging_face = os.environ.get("HUGGING_FACE")
pipelineDiary = Pipeline.from_pretrained(
"pyannote/speaker-diarization-3.1",
use_auth_token=hugging_face)
if torch.cuda.is_available():
print("diarize_wav_file Using CUDA")
pipelineDiary.to(torch.device("cuda"))
else:
print("diarize_wav_file Using CPU")
def diarize_wav_file(file_name):
print("DIARIZING " + file_name)
start = time.time()
diarization = pipelineDiary(file_name, num_speakers=2)
print("Elapsed " + str(time.time() - start))
# {"waveform": audio_tensor, "sample_rate": sample_rate_tensor})
speakers = []
contSpeaker = ""
dict = None
for turn, _, speaker in diarization.itertracks(yield_label=True):
if contSpeaker != speaker:
if dict is not None:
speakers.append(dict)
dict = {'speaker': speaker, 'start': round(turn.start, 1),
'end': round(turn.end, 1)}
contSpeaker = speaker
else:
dict['end'] = round(turn.end, 1)
return speakers
def convert_mono_16khz(location, file):
sound = AudioSegment.from_file(location+file, "wav")
sound = sound.set_channels(1)
sound = sound.set_frame_rate(16000)
sound.export(location+"16khz"+file, "wav")
location = os.path.join(".", "data") + os.sep
def get_included_files():
files = os.listdir(location)
return location, files
def main():
dir_list = os.listdir(location)
for file in dir_list :
#input_file=location+file
input_file='C:\\Users\\jerry\\Downloads\\SampleCallsWave\\Tech Support Help from Call Center Experts1.wav'
# apply pretrained pipeline
# Pass the audio tensor and sample rate to the pipeline
speakers = diarize_wav_file(input_file)
speakers = segment_wave_files(speakers, input_file)
transcript = transcribe_segments(speakers)
print(
"---------------------------------------------------------------------")
pprint(transcript)
print("---------------------------------------------------------------------")
summary = transcript_analysis(transcript)
pprint(summary) #.encode('utf-8').decode('utf-8'))
print("\n\n\n\n\n\n\n")
def convertMp3ToWav(file) :
# convert mp3 file to a wav file
sound = AudioSegment.from_mp3(file)
# sound.export(output_file, format="wav")
sample_rate = sound.frame_count() / sound.duration_seconds
print(sample_rate)
duration = sound.duration_seconds
sound = sound.set_frame_rate(16000)
sound = sound.set_channels(1)
outFile = os.path.splitext(file)[0]+".wav"
sound.export(outFile, format="wav")
return outFile
main()
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