m.giller/tavern-talk
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Maximilian Giller 2025-03-26 22:29:22 +01:00
parent d62195a590
commit 7b74c8372a
4 changed files with 52 additions and 29 deletions
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3
.gitignore vendored Normal file
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@ -0,0 +1,3 @@
.vscode/
venv/
tmp_spkrec/

32
poetry.lock generated
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@ -575,6 +575,36 @@ files = [
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@ -1593,4 +1623,4 @@ zstd = ["zstandard (>=0.18.0)"]
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2
pyproject.toml
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@ -13,6 +13,8 @@ speechbrain = "^1.0.2"
matplotlib = "^3.9.2"
torch = "^2.5.1"
torchaudio = "^2.5.1"
llvmlite = "^0.43.0"
setuptools = "^75.3.0"
[build-system]

44
tavern_talk/diarization.py
View file

@ -1,11 +1,13 @@
import torch
from pyannote.audio import Pipeline
import whisper
import librosa
import numpy as np
import matplotlib.pyplot as plt
import librosa.display
from pyannote.core import Segment, Annotation
AUDIO_FILE = "2024-07-29_audio.wav"
filename = (AUDIO_FILE[::-1].split(".")[1].split("/")[0].split("\\")[0])[::-1]
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
# Load Whisper model for transcription
whisper_model = whisper.load_model("large")
@ -28,7 +30,7 @@ def perform_diarization(audio_path) -> Pipeline:
diarization = pipeline(audio_path, min_speakers=5, max_speakers=7)
# dump the diarization output to disk using RTTM format
with open("diarization.rttm", "w") as rttm:
with open(f"diarization_{filename}.rttm", "w") as rttm:
diarization.write_rttm(rttm)
print("Finished diarization")
@ -37,37 +39,23 @@ def perform_diarization(audio_path) -> Pipeline:
# Load audio and perform both transcription and diarization
audio_path = "mid_audio.wav"
transcription_segments = transcribe_audio(audio_path)
diarization: Pipeline = perform_diarization(audio_path)
transcription_segments = transcribe_audio(AUDIO_FILE)
diarization: Pipeline = perform_diarization(AUDIO_FILE)
# Print speaker and corresponding text
print("\nSpeaker and Text Segments:")
diarization_with_text = []
for segment in transcription_segments:
start, end, text = segment
for spk_segment, _, speaker_label in diarization.itertracks(yield_label=True):
if spk_segment.start < end and spk_segment.end > start:
print(f"Speaker {speaker_label}: {text}")
diarization_with_text.append(f"Speaker {speaker_label}: {text}")
break
# Load audio for plotting
audio, sr = librosa.load(audio_path, sr=None)
# Plot the audio waveform and speaker segments
plt.figure(figsize=(12, 6))
librosa.display.waveshow(audio, sr=sr, alpha=0.5, color="gray")
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")
plt.title("Speaker Diarization with Transcription")
with open(f"transcript-diarization_{filename}.txt", "w") as fp:
fp.writelines(f"{l}\n" for l in diarization_with_text)
# Plot speaker segments and add transcription text
for segment, _, label in diarization.itertracks(yield_label=True):
start, end = segment.start, segment.end
plt.plot([start, end], [0.9, 0.9], label=f"Speaker {label}")
print("\n".join(diarization_with_text))
# Avoid duplicate labels in legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys(), loc="upper right")
plt.show()
print("Saved diarization")