Question: I am running a VM instance on google cloud. My goal is to apply speaker diarization to several .wav files stored on cloud buckets. I have tried the following alternatives with the subsequent problems:  Speaker diarization on Google's API. This seems to go fast but the results make no sense at all. I've already seen similar issues and I opened a thread myself but I get no answer... The output of this only returns maximum of two speakers with random labels. Here is the code I tried in python:  from google.cloud import speech_v1p1beta1 as speech from google.cloud import storage import os import json import sys storage_client = storage.Client() client = speech.SpeechClient() if "--channel" in sys.argv: index = sys.argv.index("--channel") + 1 if index < len(sys.argv): channel = sys.argv[index] print("Channel:", channel) else: print("--channel option requires a value") audio_folder=f'audio_{channel}' # channel='tve' transcript_folder=f'transcript_output' bucket = storage_client.bucket(audio_folder) bucket2 = storage_client.bucket(transcript_folder) wav_files=[i.name for i in bucket.list_blobs()] json_files=[i.name.split(f'{channel}/')[-1] for i in bucket2.list_blobs(prefix=channel)] for file in wav_files: if not file.endswith('.wav'): continue transcript_name=file.replace('.wav','.json') if transcript_name in json_files: continue gcs_uri = f"gs://{audio_folder}/{file}" # gcs_uri = f"gs://{audio_folder}/out2.wav" audio = speech.RecognitionAudio(uri=gcs_uri) diarization_config = speech.SpeakerDiarizationConfig( enable_speaker_diarization=True, min_speaker_count=2, #max_speaker_count=10, ) config = speech.RecognitionConfig( encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16, #sample_rate_hertz=8000, language_code="es-ES", diarization_config=diarization_config, #audio_channel_count = 2, ) print("Waiting for operation to complete...") operation = client.long_running_recognize(config=config, audio=audio) response=operation.result() result = response.results[-1] # print(result) # print(type(result)) with open(transcript_name,'w') as f: json.dump(str(result),f) # transcript_name=file.replace('.wav','.txt') # result = response.results[-1] # with open(transcript_name,'w') as f: # f.write(result) os.system(f'gsutil cp {transcript_name} gs://transcript_output/{channel}') os.remove(transcript_name) print(f'File {file} processed. ')  No matter how the max_speaker or min are changed, results are the same.  pyannote:  As the above did not work, I decided to try with pyannote. The performance of it is very nice but there is one problem, it is extremely slow. For a wav file of 30 mins it takes more than 3 hours to finish the diarization. Here is my code:  #import packages import os from datetime import datetime import pandas as pd from pyannote.audio import Pipeline from pyannote.audio import Model from pyannote.core.json import dump from pyannote.core.json import load from pyannote.core.json import loads from pyannote.core.json import load_from import subprocess from pyannote.database.util import load_rttm from google.cloud import speech_v1p1beta1 as speech from google.cloud import storage import sys # channel='a3' storage_client = storage.Client() if "--channel" in sys.argv: index = sys.argv.index("--channel") + 1 if index < len(sys.argv): channel = sys.argv[index] print("Channel:", channel) else: print("--channel option requires a value") audio_folder=f'audio_{channel}' transcript_folder=f'transcript_{channel}' bucket = storage_client.bucket(audio_folder) bucket2 = storage_client.bucket(transcript_folder) wav_files=[i.name for i in bucket.list_blobs()] rttm_files=[i.name for i in bucket2.list_blobs()] token="XXX" pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1", use_auth_token=token) # this load the model model = Model.from_pretrained("pyannote/segmentation", use_auth_token=token) for file in wav_files: if not file.endswith('.wav'): continue rttm_name=file.replace('.wav','.rttm') if rttm_name in rttm_files: continue if '2023' not in file: continue print(f'Doing file {file}') gcs_uri = f"gs://{audio_folder}/{file}" os.system(f'gsutil cp {gcs_uri} {file}') diarization = pipeline(file) with open(rttm_name, "w") as rttm: diarization.write_rttm(rttm) os.system(f'gsutil cp {rttm_name} gs://transcript_{channel}/{rttm_name}') os.remove(file) os.remove(rttm_name)  I am running this with python3.9 on a VM instance with GPU NVIDIA-T4. Is this normal? I've seen that pyannote.audio is kinda slow on the factor of 1x or so, this time is much more than that given that, in theory, it should be running on a dedicated GPU for it... Are there any faster alternatives? Any way to improve the code or design a VM that might increase speed?

Answer: In order to make this work quickly on a GPU (Google colab is used as an example): You need to first install pyannote: !pip install -qq https://github.com/pyannote/pyannote-audio/archive/refs/heads/develop.zip  And then: from pyannote.audio import Pipeline import torch pipeline = Pipeline.from_pretrained( "pyannote/speaker-diarization@2.1", use_auth_token='your hugging face token here') pipeline.to(torch.device('cuda')) # switch to gpu diarization = pipeline(audio_file_path)