File generation versus terminal output

[Robert-K-S]

I notice that whisper tries to generate files and that the types of files to be generated can be adjusted with --output_format {txt,vtt,srt,tsv,json,all}, -f {txt,vtt,srt,tsv,json,all}.

What is the difference between these files and a file that is generated using the > character in the command line, e.g., using

sudo -u www-data /home/username/.local/bin/whisper '/home/username/public_html/filename.mp3' --model tiny --language English > '/home/username/filename.txt'

I ask because I wonder if under certain conditions whisper will not have the permissions to write its own output files (e.g., when running as user www-data, as in the above example) but would nevertheless be able to write terminal output to a file using the > character.

And if that is the case, I wonder whether one of the options for the --output_format command line switch should be "none".

[jongwook]

It doesn't support --output-format none, and likely we won't plan to -- if www-user can write to /tmp, you can specify something like --output_dir /tmp/whisper.

Alternatively, you can write a separate Python script doing something similar to the cli() function:

whisper/whisper/transcribe.py

Lines 374 to 453 in 248b6cb

	def cli():
	from . import available_models
	# 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", choices=available_models(), 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("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
	parser.add_argument("--output_format", "-f", type=str, default="all", choices=["txt", "vtt", "srt", "tsv", "json", "all"], 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")
	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=None, 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=None, 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("--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=True, 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("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them")
	parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word")
	parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word")
	parser.add_argument("--highlight_words", type=str2bool, default=False, help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt")
	parser.add_argument("--max_line_width", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of characters in a line before breaking the line")
	parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of lines in a segment")
	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")
	# fmt: on
	args = parser.parse_args().__dict__
	model_name: str = args.pop("model")
	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")
	os.makedirs(output_dir, exist_ok=True)
	if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
	if args["language"] is not None:
	warnings.warn(
	f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead."
	)
	args["language"] = "en"
	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]
	if (threads := args.pop("threads")) > 0:
	torch.set_num_threads(threads)
	from . import load_model
	model = load_model(model_name, device=device, download_root=model_dir)
	writer = get_writer(output_format, output_dir)
	word_options = ["highlight_words", "max_line_count", "max_line_width"]
	if not args["word_timestamps"]:
	for option in word_options:
	if args[option]:
	parser.error(f"--{option} requires --word_timestamps True")
	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}
	for audio_path in args.pop("audio"):
	result = transcribe(model, audio_path, temperature=temperature, **args)
	writer(result, audio_path, writer_args)

but skip the part saving the outputs.