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import gradio as gr
import pytest
import os
from modules.whisper.data_classes import *
from modules.vad.silero_vad import SileroVAD
from test_config import *
from test_transcription import download_file, test_transcribe
from faster_whisper.vad import VadOptions, get_speech_timestamps
@pytest.mark.parametrize(
"whisper_type,vad_filter,bgm_separation,diarization",
[
(WhisperImpl.WHISPER.value, True, False, False),
(WhisperImpl.FASTER_WHISPER.value, True, False, False),
(WhisperImpl.INSANELY_FAST_WHISPER.value, True, False, False)
]
)
def test_vad_pipeline(
whisper_type: str,
vad_filter: bool,
bgm_separation: bool,
diarization: bool,
):
test_transcribe(whisper_type, vad_filter, bgm_separation, diarization)
@pytest.mark.parametrize(
"threshold,min_speech_duration_ms,min_silence_duration_ms",
[
(0.5, 250, 2000),
]
)
def test_vad(
threshold: float,
min_speech_duration_ms: int,
min_silence_duration_ms: int
):
audio_path_dir = os.path.join(WEBUI_DIR, "tests")
audio_path = os.path.join(audio_path_dir, "jfk.wav")
if not os.path.exists(audio_path):
download_file(TEST_FILE_DOWNLOAD_URL, audio_path_dir)
vad_model = SileroVAD()
vad_model.update_model()
audio, speech_chunks = vad_model.run(
audio=audio_path,
vad_parameters=VadOptions(
threshold=threshold,
min_silence_duration_ms=min_silence_duration_ms,
min_speech_duration_ms=min_speech_duration_ms
)
)
assert speech_chunks
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