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import argparse
import gc
import os
import warnings
import numpy as np
import torch
from .alignment import align, load_align_model
from .asr import load_model
from .audio import load_audio
from .diarize import DiarizationPipeline, assign_word_speakers
from .utils import (LANGUAGES, TO_LANGUAGE_CODE, get_writer, optional_float,
optional_int, str2bool)
def cli():
# fmt: off
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
parser.add_argument("--model", default="small", help="name of the Whisper model to use")
parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
parser.add_argument("--device_index", default=0, type=int, help="device index to use for FasterWhisper inference")
parser.add_argument("--batch_size", default=8, type=int, help="the preferred batch size for inference")
parser.add_argument("--compute_type", default="float16", type=str, choices=["float16", "float32", "int8"], help="compute type for computation")
parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
parser.add_argument("--output_format", "-f", type=str, default="all", choices=["all", "srt", "vtt", "txt", "tsv", "json", "aud"], help="format of the output file; if not specified, all available formats will be produced")
parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection")
# alignment params
parser.add_argument("--align_model", default=None, help="Name of phoneme-level ASR model to do alignment")
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.")
parser.add_argument("--no_align", action='store_true', help="Do not perform phoneme alignment")
parser.add_argument("--return_char_alignments", action='store_true', help="Return character-level alignments in the output json file")
# vad params
parser.add_argument("--vad_onset", type=float, default=0.500, help="Onset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected")
parser.add_argument("--vad_offset", type=float, default=0.363, help="Offset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected.")
parser.add_argument("--chunk_size", type=int, default=30, help="Chunk size for merging VAD segments. Default is 30, reduce this if the chunk is too long.")
# diarization params
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, help="Minimum number of speakers to in audio file")
parser.add_argument("--max_speakers", default=None, type=int, help="Maximum number of speakers to in audio file")
parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero")
parser.add_argument("--patience", type=float, default=1.0, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search")
parser.add_argument("--length_penalty", type=float, default=1.0, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default")
parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
parser.add_argument("--suppress_numerals", action="store_true", help="whether to suppress numeric symbols and currency symbols during sampling, since wav2vec2 cannot align them correctly")
parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
parser.add_argument("--condition_on_previous_text", type=str2bool, default=False, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
parser.add_argument("--max_line_width", type=optional_int, default=None, help="(not possible with --no_align) the maximum number of characters in a line before breaking the line")
parser.add_argument("--max_line_count", type=optional_int, default=None, help="(not possible with --no_align) the maximum number of lines in a segment")
parser.add_argument("--highlight_words", type=str2bool, default=False, help="(not possible with --no_align) underline each word as it is spoken in srt and vtt")
parser.add_argument("--segment_resolution", type=str, default="sentence", choices=["sentence", "chunk"], help="(not possible with --no_align) the maximum number of characters in a line before breaking the line")
parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
parser.add_argument("--hf_token", type=str, default=None, help="Hugging Face Access Token to access PyAnnote gated models")
parser.add_argument("--print_progress", type=str2bool, default = False, help = "if True, progress will be printed in transcribe() and align() methods.")
# fmt: on
args = parser.parse_args().__dict__
model_name: str = args.pop("model")
batch_size: int = args.pop("batch_size")
model_dir: str = args.pop("model_dir")
output_dir: str = args.pop("output_dir")
output_format: str = args.pop("output_format")
device: str = args.pop("device")
device_index: int = args.pop("device_index")
compute_type: str = args.pop("compute_type")
# model_flush: bool = args.pop("model_flush")
os.makedirs(output_dir, exist_ok=True)
align_model: str = args.pop("align_model")
interpolate_method: str = args.pop("interpolate_method")
no_align: bool = args.pop("no_align")
task : str = args.pop("task")
if task == "translate":
# translation cannot be aligned
no_align = True
return_char_alignments: bool = args.pop("return_char_alignments")
hf_token: str = args.pop("hf_token")
vad_onset: float = args.pop("vad_onset")
vad_offset: float = args.pop("vad_offset")
chunk_size: int = args.pop("chunk_size")
diarize: bool = args.pop("diarize")
min_speakers: int = args.pop("min_speakers")
max_speakers: int = args.pop("max_speakers")
print_progress: bool = args.pop("print_progress")
if args["language"] is not None:
args["language"] = args["language"].lower()
if args["language"] not in LANGUAGES:
if args["language"] in TO_LANGUAGE_CODE:
args["language"] = TO_LANGUAGE_CODE[args["language"]]
else:
raise ValueError(f"Unsupported language: {args['language']}")
if model_name.endswith(".en") and args["language"] != "en":
if args["language"] is not None:
warnings.warn(
f"{model_name} is an English-only model but received '{args['language']}'; using English instead."
)
args["language"] = "en"
align_language = args["language"] if args["language"] is not None else "en" # default to loading english if not specified
temperature = args.pop("temperature")
if (increment := args.pop("temperature_increment_on_fallback")) is not None:
temperature = tuple(np.arange(temperature, 1.0 + 1e-6, increment))
else:
temperature = [temperature]
faster_whisper_threads = 4
if (threads := args.pop("threads")) > 0:
torch.set_num_threads(threads)
faster_whisper_threads = threads
asr_options = {
"beam_size": args.pop("beam_size"),
"patience": args.pop("patience"),
"length_penalty": args.pop("length_penalty"),
"temperatures": temperature,
"compression_ratio_threshold": args.pop("compression_ratio_threshold"),
"log_prob_threshold": args.pop("logprob_threshold"),
"no_speech_threshold": args.pop("no_speech_threshold"),
"condition_on_previous_text": False,
"initial_prompt": args.pop("initial_prompt"),
"suppress_tokens": [int(x) for x in args.pop("suppress_tokens").split(",")],
"suppress_numerals": args.pop("suppress_numerals"),
}
writer = get_writer(output_format, output_dir)
word_options = ["highlight_words", "max_line_count", "max_line_width"]
if no_align:
for option in word_options:
if args[option]:
parser.error(f"--{option} not possible with --no_align")
if args["max_line_count"] and not args["max_line_width"]:
warnings.warn("--max_line_count has no effect without --max_line_width")
writer_args = {arg: args.pop(arg) for arg in word_options}
# Part 1: VAD & ASR Loop
results = []
tmp_results = []
# model = load_model(model_name, device=device, download_root=model_dir)
model = load_model(model_name, device=device, device_index=device_index, download_root=model_dir, compute_type=compute_type, language=args['language'], asr_options=asr_options, vad_options={"vad_onset": vad_onset, "vad_offset": vad_offset}, task=task, threads=faster_whisper_threads)
for audio_path in args.pop("audio"):
audio = load_audio(audio_path)
# >> VAD & ASR
print(">>Performing transcription...")
result = model.transcribe(audio, batch_size=batch_size, chunk_size=chunk_size, print_progress=print_progress)
results.append((result, audio_path))
# Unload Whisper and VAD
del model
gc.collect()
torch.cuda.empty_cache()
# Part 2: Align Loop
if not no_align:
tmp_results = results
results = []
align_model, align_metadata = load_align_model(align_language, device, model_name=align_model)
for result, audio_path in tmp_results:
# >> Align
if len(tmp_results) > 1:
input_audio = audio_path
else:
# lazily load audio from part 1
input_audio = audio
if align_model is not None and len(result["segments"]) > 0:
if result.get("language", "en") != align_metadata["language"]:
# load new language
print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
align_model, align_metadata = load_align_model(result["language"], device)
print(">>Performing alignment...")
result = align(result["segments"], align_model, align_metadata, input_audio, device, interpolate_method=interpolate_method, return_char_alignments=return_char_alignments, print_progress=print_progress)
results.append((result, audio_path))
# Unload align model
del align_model
gc.collect()
torch.cuda.empty_cache()
# >> Diarize
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...")
tmp_results = results
print(">>Performing diarization...")
results = []
diarize_model = DiarizationPipeline(use_auth_token=hf_token, device=device)
for result, input_audio_path in tmp_results:
diarize_segments = diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
result = assign_word_speakers(diarize_segments, result)
results.append((result, input_audio_path))
# >> Write
for result, audio_path in results:
result["language"] = align_language
writer(result, audio_path, writer_args)
if __name__ == "__main__":
cli()
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