tqdm not working like it strucks at 74% for some video

[hacker-saran]

import argparse
import os
import traceback
import warnings
from typing import TYPE_CHECKING, Optional, Tuple, Union

import numpy as np
import torch
import tqdm

from .audio import (
FRAMES_PER_SECOND,
HOP_LENGTH,
N_FRAMES,
N_SAMPLES,
SAMPLE_RATE,
log_mel_spectrogram,
pad_or_trim,
)
from .decoding import DecodingOptions, DecodingResult
from .timing import add_word_timestamps
from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
from .utils import (
exact_div,
format_timestamp,
get_writer,
make_safe,
optional_float,
optional_int,
str2bool,
)

if TYPE_CHECKING:
from .model import Whisper

def transcribe(
model: "Whisper",
audio: Union[str, np.ndarray, torch.Tensor],
*,
verbose: Optional[bool] = None,
temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
compression_ratio_threshold: Optional[float] = 2.4,
logprob_threshold: Optional[float] = -1.0,
no_speech_threshold: Optional[float] = 0.6,
condition_on_previous_text: bool = True,
initial_prompt: Optional[str] = None,
word_timestamps: bool = False,
prepend_punctuations: str = ""'“¿([{-",
append_punctuations: str = ""'.。,，!！?？:：”)]}、",
**decode_options,
):
"""
Transcribe an audio file using Whisper

Parameters
----------
model: Whisper
    The Whisper model instance

audio: Union[str, np.ndarray, torch.Tensor]
    The path to the audio file to open, or the audio waveform

verbose: bool
    Whether to display the text being decoded to the console. If True, displays all the details,
    If False, displays minimal details. If None, does not display anything

temperature: Union[float, Tuple[float, ...]]
    Temperature for sampling. It can be a tuple of temperatures, which will be successively used
    upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.

compression_ratio_threshold: float
    If the gzip compression ratio is above this value, treat as failed

logprob_threshold: float
    If the average log probability over sampled tokens is below this value, treat as failed

no_speech_threshold: float
    If the no_speech probability is higher than this value AND the average log probability
    over sampled tokens is below `logprob_threshold`, consider the segment as silent

condition_on_previous_text: bool
    if True, the previous output of the model is provided 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, such as repetition looping or timestamps going out of sync.

word_timestamps: bool
    Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
    and include the timestamps for each word in each segment.

prepend_punctuations: str
    If word_timestamps is True, merge these punctuation symbols with the next word

append_punctuations: str
    If word_timestamps is True, merge these punctuation symbols with the previous word

initial_prompt: Optional[str]
    Optional text to provide as a prompt for the first window. This can be used to provide, or
    "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
    to make it more likely to predict those word correctly.

decode_options: dict
    Keyword arguments to construct `DecodingOptions` instances

Returns
-------
A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
the spoken language ("language"), which is detected when `decode_options["language"]` is None.
"""
dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
if model.device == torch.device("cpu"):
    if torch.cuda.is_available():
        warnings.warn("Performing inference on CPU when CUDA is available")
    if dtype == torch.float16:
        warnings.warn("FP16 is not supported on CPU; using FP32 instead")
        dtype = torch.float32

if dtype == torch.float32:
    decode_options["fp16"] = False

# Pad 30-seconds of silence to the input audio, for slicing
mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES)
content_frames = mel.shape[-1] - N_FRAMES

if decode_options.get("language", None) is None:
    if not model.is_multilingual:
        decode_options["language"] = "en"
    else:
        if verbose:
            print(
                "Detecting language using up to the first 30 seconds. Use `--language` to specify the language"
            )
        mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
        _, probs = model.detect_language(mel_segment)
        decode_options["language"] = max(probs, key=probs.get)
        if verbose is not None:
            print(
                f"Detected language: {LANGUAGES[decode_options['language']].title()}"
            )

language: str = decode_options["language"]
task: str = decode_options.get("task", "transcribe")
tokenizer = get_tokenizer(
    model.is_multilingual,
    num_languages=model.num_languages,
    language=language,
    task=task,
)

if word_timestamps and task == "translate":
    warnings.warn("Word-level timestamps on translations may not be reliable.")

def decode_with_fallback(segment: torch.Tensor) -> DecodingResult:
    temperatures = (
        [temperature] if isinstance(temperature, (int, float)) else temperature
    )
    decode_result = None

    for t in temperatures:
        kwargs = {**decode_options}
        if t > 0:
            # disable beam_size and patience when t > 0
            kwargs.pop("beam_size", None)
            kwargs.pop("patience", None)
        else:
            # disable best_of when t == 0
            kwargs.pop("best_of", None)

        options = DecodingOptions(**kwargs, temperature=t)
        decode_result = model.decode(segment, options)

        needs_fallback = False
        if (
            compression_ratio_threshold is not None
            and decode_result.compression_ratio > compression_ratio_threshold
        ):
            needs_fallback = True  # too repetitive
        if (
            logprob_threshold is not None
            and decode_result.avg_logprob < logprob_threshold
        ):
            needs_fallback = True  # average log probability is too low
        if (
            no_speech_threshold is not None
            and decode_result.no_speech_prob > no_speech_threshold
        ):
            needs_fallback = False  # silence
        if not needs_fallback:
            break

    return decode_result

seek = 0
input_stride = exact_div(
    N_FRAMES, model.dims.n_audio_ctx
)  # mel frames per output token: 2
time_precision = (
    input_stride * HOP_LENGTH / SAMPLE_RATE
)  # time per output token: 0.02 (seconds)
all_tokens = []
all_segments = []
prompt_reset_since = 0

if initial_prompt is not None:
    initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
    all_tokens.extend(initial_prompt_tokens)
else:
    initial_prompt_tokens = []

def new_segment(
    *, start: float, end: float, tokens: torch.Tensor, result: DecodingResult
):
    tokens = tokens.tolist()
    text_tokens = [token for token in tokens if token < tokenizer.eot]
    return {
        "seek": seek,
        "start": start,
        "end": end,
        "text": tokenizer.decode(text_tokens),
        "tokens": tokens,
        "temperature": result.temperature,
        "avg_logprob": result.avg_logprob,
        "compression_ratio": result.compression_ratio,
        "no_speech_prob": result.no_speech_prob,
    }

# show the progress bar when verbose is False (if True, transcribed text will be printed)
with tqdm.tqdm(
    total=content_frames, unit="frames", disable=verbose is not False, desc = "Model Running",
) as pbar:
    last_speech_timestamp = 0.0
    while seek < content_frames:
        time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
        mel_segment = mel[:, seek : seek + N_FRAMES]
        segment_size = min(N_FRAMES, content_frames - seek)
        segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
        mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype)

        decode_options["prompt"] = all_tokens[prompt_reset_since:]
        result: DecodingResult = decode_with_fallback(mel_segment)
        tokens = torch.tensor(result.tokens)

        if no_speech_threshold is not None:
            # no voice activity check
            should_skip = result.no_speech_prob > no_speech_threshold
            if (
                logprob_threshold is not None
                and result.avg_logprob > logprob_threshold
            ):
                # don't skip if the logprob is high enough, despite the no_speech_prob
                should_skip = False

            if should_skip:
                seek += segment_size  # fast-forward to the next segment boundary
                continue

        previous_seek = seek
        current_segments = []

        timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin)
        single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True]

        consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0]
        consecutive.add_(1)
        if len(consecutive) > 0:
            # if the output contains two consecutive timestamp tokens
            slices = consecutive.tolist()
            if single_timestamp_ending:
                slices.append(len(tokens))

            last_slice = 0
            for current_slice in slices:
                sliced_tokens = tokens[last_slice:current_slice]
                start_timestamp_pos = (
                    sliced_tokens[0].item() - tokenizer.timestamp_begin
                )
                end_timestamp_pos = (
                    sliced_tokens[-1].item() - tokenizer.timestamp_begin
                )
                current_segments.append(
                    new_segment(
                        start=time_offset + start_timestamp_pos * time_precision,
                        end=time_offset + end_timestamp_pos * time_precision,
                        tokens=sliced_tokens,
                        result=result,
                    )
                )
                last_slice = current_slice

            if single_timestamp_ending:
                # single timestamp at the end means no speech after the last timestamp.
                seek += segment_size
            else:
                # otherwise, ignore the unfinished segment and seek to the last timestamp
                last_timestamp_pos = (
                    tokens[last_slice - 1].item() - tokenizer.timestamp_begin
                )
                seek += last_timestamp_pos * input_stride
        else:
            duration = segment_duration
            timestamps = tokens[timestamp_tokens.nonzero().flatten()]
            if (
                len(timestamps) > 0
                and timestamps[-1].item() != tokenizer.timestamp_begin
            ):
                # no consecutive timestamps but it has a timestamp; use the last one.
                last_timestamp_pos = (
                    timestamps[-1].item() - tokenizer.timestamp_begin
                )
                duration = last_timestamp_pos * time_precision

            current_segments.append(
                new_segment(
                    start=time_offset,
                    end=time_offset + duration,
                    tokens=tokens,
                    result=result,
                )
            )
            seek += segment_size

        if word_timestamps:
            add_word_timestamps(
                segments=current_segments,
                model=model,
                tokenizer=tokenizer,
                mel=mel_segment,
                num_frames=segment_size,
                prepend_punctuations=prepend_punctuations,
                append_punctuations=append_punctuations,
                last_speech_timestamp=last_speech_timestamp,
            )
            word_end_timestamps = [
                w["end"] for s in current_segments for w in s["words"]
            ]
            if len(word_end_timestamps) > 0:
                last_speech_timestamp = word_end_timestamps[-1]
            if not single_timestamp_ending and len(word_end_timestamps) > 0:
                seek_shift = round(
                    (word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND
                )
                if seek_shift > 0:
                    seek = previous_seek + seek_shift

        if verbose:
            for segment in current_segments:
                start, end, text = segment["start"], segment["end"], segment["text"]
                line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}"
                print(make_safe(line))

        # if a segment is instantaneous or does not contain text, clear it
        for i, segment in enumerate(current_segments):
            if segment["start"] == segment["end"] or segment["text"].strip() == "":
                segment["text"] = ""
                segment["tokens"] = []
                segment["words"] = []

        all_segments.extend(
            [
                {"id": i, **segment}
                for i, segment in enumerate(
                    current_segments, start=len(all_segments)
                )
            ]
        )
        all_tokens.extend(
            [token for segment in current_segments for token in segment["tokens"]]
        )

        if not condition_on_previous_text or result.temperature > 0.5:
            # do not feed the prompt tokens if a high temperature was used
            prompt_reset_since = len(all_tokens)

        # update progress bar
        pbar.update(min(content_frames, seek) - previous_seek)

return dict(
    text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]),
    segments=all_segments,
    language=language,
)

def cli():
from . import available_models

def valid_model_name(name):
    if name in available_models() or os.path.exists(name):
        return name
    raise ValueError(
        f"model should be one of {available_models()} or path to a model checkpoint"
    )

# 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", type=valid_model_name, 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("--max_words_per_line", type=optional_int, default=None, help="(requires --word_timestamps True, no effect with --max_line_width) the maximum number of words 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",
    "max_words_per_line",
]
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")
if args["max_words_per_line"] and args["max_line_width"]:
    warnings.warn("--max_words_per_line has no effect with --max_line_width")
writer_args = {arg: args.pop(arg) for arg in word_options}
for audio_path in args.pop("audio"):
    try:
        result = transcribe(model, audio_path, temperature=temperature, **args)
        writer(result, audio_path, **writer_args)
    except Exception as e:
        traceback.print_exc()
        print(f"Skipping {audio_path} due to {type(e).__name__}: {str(e)}")

if name == "main":
cli()