pyannote-audio VS Resemblyzer

pyannote/pyannote-audio

Open Source. I've found this to be pretty nice, which is just a wrapper on some hugging face models https://github.com/pyannote/pyannote-audio

(not the creator, but I've built something similar for personal use)

This is a great library for determining which speaker is speaking during each time in an audio file (this is called speaker diarization); I imagine they used it or something like it. Works really well out of the box!

https://github.com/pyannote/pyannote-audio

Do you think pyannote could be implemented in the Pro version of the app to support diarization?

Free startup idea: Use Whisper with pyannote-audio[0]’s speaker diarization. Upload a recording, get back a multi-speaker annotated transcription.

Make a JSON API and I’ll be your first customer.

[0] https://github.com/pyannote/pyannote-audio

Whisper can’t, but pyannote-audio can. I’ve seen a couple of prototypes out there which link the two together.

You can use pyannote python library. It will identify different speakers from audio and will create small audio files with those speakers.

This Colab notebook is basically a standard template (with small changes) provided by pyannote-audio, the library implementing the speaker diarization functionality we need. (template)

Resemblyzer allows us to derive high-level representation of voice through a deep learning model. It simplifies the life of developers by enabling them to convert audio clips into vectors with just a few lines of code, eliminating the need for neural networks. See official github repository.

I want to perform a modification to Resemblyzer's speaker diarization script to cut out parts of audio where a specific speaker isn't present. While the graph generated by the original demo seems alright, the timestamps at which it chooses to cut the audio are off. I got this conclusion because when I outputted all the timestamp information, my 22 minute long video came out to be 1036 seconds long. Also, the variable I'm indexing the time by seems to be a collection of "wave partials as a list of slices ", as represented by the function that generates its value. Furthermore, the function I was modifying to get the time said that the intervals were non-reliable. This is bad, because as you will see below in my code, when cutting the video with ffmpeg, I treat them as if these were one-to-one with the video: from resemblyzer import preprocess_wav, VoiceEncoder from demo_utils import * from pathlib import Path from os import listdir, system from os.path import join def Diarization(path, file, segments): wav_fpath = Path(join(path, file)) wav = preprocess_wav(wav_fpath) speaker_names = ["Peter"] speaker_wavs = [wav[int(s[0] * sampling_rate):int(s[1]) * sampling_rate] for s in segments] encoder = VoiceEncoder("cpu") print("Running the continuous embedding on cpu, this might take a while...") _, cont_embeds, wav_splits = encoder.embed_utterance(wav, return_partials=True, rate=16) speaker_embeds = [encoder.embed_utterance(speaker_wav) for speaker_wav in speaker_wavs] similarity_dict = {name: cont_embeds @ speaker_embed for name, speaker_embed in zip(speaker_names, speaker_embeds)} times = [((s.start + s.stop) / 2) / sampling_rate for s in wav_splits] keep = True cutTimes = [[times[0], times[len(wav_splits) - 1]]] #similar = open("similarities.txt", "w+") for i in range(len(wav_splits)): similarities = [s[i] for s in similarity_dict.values()] best = np.argmax(similarities) name, similarity = list(similarity_dict.keys())[best], similarities[best] #similar.write(f"{times[i]} - {similarity}\n") if similarity > 0.65: if not keep: cutTimes.append([times[i], times[len(wav_splits) - 1]]) keep = True else: ⠀ if keep: cutTimes[len(cutTimes) - 1][1] = times[i] keep = False #similar.close() cutCommand = "" for num, seg in enumerate(cutTimes): if num == 0: cutCommand += f"between(t,{seg[0]},{seg[1]})" continue cutCommand += f"+between(t,{seg[0]},{seg[1]})" addMe = "Cut - " print(f"ffmpeg -i \"{join(path, file)}\" -af \"aselect='{cutCommand}',asetpts=N/SR/TB\" \"{join(path, addMe+file)}\"") system(f"ffmpeg -y -i \"{join(path, file)}\" -af \"aselect='{cutCommand}',asetpts=N/SR/TB\" \"{join(path, addMe+file)}\"") path = r'C:\Users\mlfre\OneDrive\Desktop\Resemblyzer\Resemblyzer-master\audio_data' for file in listdir(path): #if ".mp3" in file or ".wav" in file or ".mp4" in file: if file == "peter.mp3": segments = [[12, 21]] Diarization(path, file, segments) Since the graph's values were accurate in real time, if I could just manage to get the time intervals accurate in real time as well, I would be golden. Unfortunately, I do not know how to translate from iterating over a list of wav partials as slices, to the length in time of a wave file.

I've tried Resemblyzer's method, yet it always either cut out too much of his voice, or included too much of others. It also required that i have a clip of him talking, and the quality of that clip heavily impacted its performance.

Try this: https://github.com/resemble-ai/Resemblyzer