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update readme

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Max Bain
2023-02-01 22:09:11 +00:00
parent 29e95b746b
commit 0a3fd11562
4 changed files with 52 additions and 36 deletions
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14
whisperx/transcribe.py
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@ -585,10 +585,9 @@ def cli():
parser.add_argument("--interpolate_method", default="nearest", choices=["nearest", "linear", "ignore"], help="For word .srt, method to assign timestamps to non-aligned words, or merge them into neighbouring.")
# vad params
parser.add_argument("--vad_filter", action="store_true", help="Whether to first perform VAD filtering to target only transcribe within VAD. Produces more accurate alignment + timestamp, requires more GPU memory & compute.")
parser.add_argument("--vad_input", default=None, type=str)
parser.add_argument("--parallel_bs", default=-1, type=int, help="Enable parallel transcribing if > 1")
# diarization params
parser.add_argument("--diarize", action='store_true')
parser.add_argument("--diarize", action="store_true", help="Apply diarization to assign speaker labels to each segment/word")
parser.add_argument("--min_speakers", default=None, type=int)
parser.add_argument("--max_speakers", default=None, type=int)
# output save params
@ -632,7 +631,6 @@ def cli():
hf_token: str = args.pop("hf_token")
vad_filter: bool = args.pop("vad_filter")
vad_input: bool = args.pop("vad_input")
parallel_bs: int = args.pop("parallel_bs")
diarize: bool = args.pop("diarize")
@ -640,9 +638,9 @@ def cli():
max_speakers: int = args.pop("max_speakers")
vad_pipeline = None
if vad_input is not None:
vad_input = pd.read_csv(vad_input, header=None, sep= " ")
elif vad_filter:
if vad_filter:
if hf_token is None:
print("Warning, no huggingface token used, needs to be saved in environment variable, otherwise will throw error loading VAD model...")
from pyannote.audio import Inference
vad_pipeline = Inference("pyannote/segmentation",
pre_aggregation_hook=lambda segmentation: segmentation,
@ -650,6 +648,8 @@ def cli():
diarize_pipeline = None
if diarize:
if hf_token is None:
print("Warning, no --hf_token used, needs to be saved in environment variable, otherwise will throw error loading diarization model...")
from pyannote.audio import Pipeline
diarize_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1",
use_auth_token=hf_token)
@ -756,7 +756,7 @@ def cli():
# save word tsv
if output_type in ["vad"]:
exp_fp = os.path.join(output_dir, audio_basename + ".sad")
wrd_segs = pd.concat([x["word-segments"] for x in result_aligned["segments"]])
wrd_segs = pd.concat([x["word-segments"] for x in result_aligned["segments"]])[['start','end']]
wrd_segs.to_csv(exp_fp, sep='\t', header=None, index=False)
if __name__ == "__main__":
cli()