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import torchaudio
import gradio as gr
from pyannote.audio import Pipeline
from pyannote.audio.pipelines.utils.hook import ProgressHook
import scipy.io.wavfile
import os
def perform_separation(audio_file_path: str):
# Instantiate the pipeline
pipeline = Pipeline.from_pretrained(
"pyannote/speech-separation-ami-1.0",
use_auth_token=HUGGINGFACE_ACCESS_TOKEN,
)
waveform, sample_rate = torchaudio.load(audio_file_path)
# Run the pipeline
with ProgressHook() as hook:
diarization, sources = pipeline(
{"waveform": waveform, "sample_rate": sample_rate}, hook=hook
)
# Save separated sources to disk as SPEAKER_XX.wav files
output_file_paths = []
for s, speaker in enumerate(diarization.labels()):
number_of_separated_sources = sources.data.shape[1]
if s >= number_of_separated_sources:
break
output_file_path = f"{speaker}.wav"
scipy.io.wavfile.write(
output_file_path, sample_rate, sources.data[:, s].numpy()
)
output_file_paths.append(output_file_path)
# Generate RTTM content
rttm_content = diarization.to_rttm()
return output_file_paths, rttm_content
def gradio_wrapper(audio_file_path: str):
output_file_paths, rttm_content = perform_separation(audio_file_path)
return output_file_paths + [rttm_content]
inputs = gr.inputs.Audio(label="Input Audio", type="filepath")
# Dynamic output for audio files
outputs = []
max_speakers = 10 # Set a reasonable maximum number of speakers
for i in range(max_speakers):
outputs.append(gr.outputs.Audio(label=f"Speaker {i+1}", type="filepath"))
# Add RTTM output
outputs.append(gr.outputs.Textbox(label="RTTM Output"))
title = "Speech Separation and Diarization"
description = "Gradio demo for Speech Separation and Diarization using Pyannote's pyannote/speech-separation-ami-1.0. To use it, simply upload your audio, or click one of the examples to load them. The app will output separated audio for each speaker and the RTTM file content."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2403.02288' target='_blank'>PixIT: Joint Training of Speaker Diarization and Speech Separation from Real-world Multi-speaker Recordings</a> | <a href='https://huggingface.co/pyannote/speech-separation-ami-1.0' '_blank'>HuggingFace Pipeline</a></p>"
examples = [["samples_audio_samples_test_mixture.wav"]]
gr.Interface(
gradio_wrapper,
inputs,
outputs,
title=title,
description=description,
article=article,
examples=examples,
).launch()
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