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import os
import whisper
import ctranslate2
import gradio as gr
import torchaudio
from abc import ABC, abstractmethod
from typing import BinaryIO, Union, Tuple, List
import numpy as np
from datetime import datetime
from faster_whisper.vad import VadOptions
import gc
from copy import deepcopy
import time
from modules.uvr.music_separator import MusicSeparator
from modules.utils.paths import (WHISPER_MODELS_DIR, DIARIZATION_MODELS_DIR, OUTPUT_DIR, DEFAULT_PARAMETERS_CONFIG_PATH,
UVR_MODELS_DIR)
from modules.utils.constants import *
from modules.utils.logger import get_logger
from modules.utils.subtitle_manager import *
from modules.utils.youtube_manager import get_ytdata, get_ytaudio
from modules.utils.files_manager import get_media_files, format_gradio_files, load_yaml, save_yaml, read_file
from modules.utils.audio_manager import validate_audio
from modules.whisper.data_classes import *
from modules.diarize.diarizer import Diarizer
from modules.vad.silero_vad import SileroVAD
logger = get_logger()
class BaseTranscriptionPipeline(ABC):
def __init__(self,
model_dir: str = WHISPER_MODELS_DIR,
diarization_model_dir: str = DIARIZATION_MODELS_DIR,
uvr_model_dir: str = UVR_MODELS_DIR,
output_dir: str = OUTPUT_DIR,
):
self.model_dir = model_dir
self.output_dir = output_dir
os.makedirs(self.output_dir, exist_ok=True)
os.makedirs(self.model_dir, exist_ok=True)
self.diarizer = Diarizer(
model_dir=diarization_model_dir
)
self.vad = SileroVAD()
self.music_separator = MusicSeparator(
model_dir=uvr_model_dir,
output_dir=os.path.join(output_dir, "UVR")
)
self.model = None
self.current_model_size = None
self.available_models = whisper.available_models()
self.available_langs = sorted(list(whisper.tokenizer.LANGUAGES.values()))
self.device = self.get_device()
self.available_compute_types = self.get_available_compute_type()
self.current_compute_type = self.get_compute_type()
@abstractmethod
def transcribe(self,
audio: Union[str, BinaryIO, np.ndarray],
progress: gr.Progress = gr.Progress(),
*whisper_params,
):
"""Inference whisper model to transcribe"""
pass
@abstractmethod
def update_model(self,
model_size: str,
compute_type: str,
progress: gr.Progress = gr.Progress()
):
"""Initialize whisper model"""
pass
def run(self,
audio: Union[str, BinaryIO, np.ndarray],
progress: gr.Progress = gr.Progress(),
file_format: str = "SRT",
add_timestamp: bool = True,
*pipeline_params,
) -> Tuple[List[Segment], float]:
"""
Run transcription with conditional pre-processing and post-processing.
The VAD will be performed to remove noise from the audio input in pre-processing, if enabled.
The diarization will be performed in post-processing, if enabled.
Due to the integration with gradio, the parameters have to be specified with a `*` wildcard.
Parameters
----------
audio: Union[str, BinaryIO, np.ndarray]
Audio input. This can be file path or binary type.
progress: gr.Progress
Indicator to show progress directly in gradio.
file_format: str
Subtitle file format between ["SRT", "WebVTT", "txt", "lrc"]
add_timestamp: bool
Whether to add a timestamp at the end of the filename.
*pipeline_params: tuple
Parameters for the transcription pipeline. This will be dealt with "TranscriptionPipelineParams" data class.
This must be provided as a List with * wildcard because of the integration with gradio.
See more info at : https://github.com/gradio-app/gradio/issues/2471
Returns
----------
segments_result: List[Segment]
list of Segment that includes start, end timestamps and transcribed text
elapsed_time: float
elapsed time for running
"""
start_time = time.time()
if not validate_audio(audio):
return [Segment()], 0
params = TranscriptionPipelineParams.from_list(list(pipeline_params))
params = self.validate_gradio_values(params)
bgm_params, vad_params, whisper_params, diarization_params = params.bgm_separation, params.vad, params.whisper, params.diarization
if bgm_params.is_separate_bgm:
music, audio, _ = self.music_separator.separate(
audio=audio,
model_name=bgm_params.uvr_model_size,
device=bgm_params.uvr_device,
segment_size=bgm_params.segment_size,
save_file=bgm_params.save_file,
progress=progress
)
if audio.ndim >= 2:
audio = audio.mean(axis=1)
if self.music_separator.audio_info is None:
origin_sample_rate = 16000
else:
origin_sample_rate = self.music_separator.audio_info.sample_rate
audio = self.resample_audio(audio=audio, original_sample_rate=origin_sample_rate)
if bgm_params.enable_offload:
self.music_separator.offload()
elapsed_time_bgm_sep = time.time() - start_time
origin_audio = deepcopy(audio)
if vad_params.vad_filter:
progress(0, desc="Filtering silent parts from audio..")
vad_options = VadOptions(
threshold=vad_params.threshold,
min_speech_duration_ms=vad_params.min_speech_duration_ms,
max_speech_duration_s=vad_params.max_speech_duration_s,
min_silence_duration_ms=vad_params.min_silence_duration_ms,
speech_pad_ms=vad_params.speech_pad_ms
)
vad_processed, speech_chunks = self.vad.run(
audio=audio,
vad_parameters=vad_options,
progress=progress
)
if vad_processed.size > 0:
audio = vad_processed
else:
vad_params.vad_filter = False
result, elapsed_time_transcription = self.transcribe(
audio,
progress,
*whisper_params.to_list()
)
if vad_params.vad_filter:
restored_result = self.vad.restore_speech_timestamps(
segments=result,
speech_chunks=speech_chunks,
)
if restored_result:
result = restored_result
else:
logger.info("VAD detected no speech segments in the audio.")
if diarization_params.is_diarize:
progress(0.99, desc="Diarizing speakers..")
result, elapsed_time_diarization = self.diarizer.run(
audio=origin_audio,
use_auth_token=diarization_params.hf_token if diarization_params.hf_token else os.environ.get("HF_TOKEN"),
transcribed_result=result,
device=diarization_params.diarization_device
)
self.cache_parameters(
params=params,
file_format=file_format,
add_timestamp=add_timestamp
)
progress(1.0, desc="Finished.")
total_elapsed_time = time.time() - start_time
return result, total_elapsed_time
def transcribe_file(self,
files: Optional[List] = None,
input_folder_path: Optional[str] = None,
include_subdirectory: Optional[str] = None,
save_same_dir: Optional[str] = None,
file_format: str = "SRT",
add_timestamp: bool = True,
progress=gr.Progress(),
*pipeline_params,
) -> Tuple[str, List]:
"""
Write subtitle file from Files
Parameters
----------
files: list
List of files to transcribe from gr.Files()
input_folder_path: Optional[str]
Input folder path to transcribe from gr.Textbox(). If this is provided, `files` will be ignored and
this will be used instead.
include_subdirectory: Optional[str]
When using `input_folder_path`, whether to include all files in the subdirectory or not
save_same_dir: Optional[str]
When using `input_folder_path`, whether to save output in the same directory as inputs or not, in addition
to the original output directory. This feature is only available when using `input_folder_path`, because
gradio only allows to use cached file path in the function yet.
file_format: str
Subtitle File format to write from gr.Dropdown(). Supported format: [SRT, WebVTT, txt]
add_timestamp: bool
Boolean value from gr.Checkbox() that determines whether to add a timestamp at the end of the subtitle filename.
progress: gr.Progress
Indicator to show progress directly in gradio.
*pipeline_params: tuple
Parameters for the transcription pipeline. This will be dealt with "TranscriptionPipelineParams" data class
Returns
----------
result_str:
Result of transcription to return to gr.Textbox()
result_file_path:
Output file path to return to gr.Files()
"""
try:
params = TranscriptionPipelineParams.from_list(list(pipeline_params))
writer_options = {
"highlight_words": True if params.whisper.word_timestamps else False
}
if input_folder_path:
files = get_media_files(input_folder_path, include_sub_directory=include_subdirectory)
if isinstance(files, str):
files = [files]
if files and isinstance(files[0], gr.utils.NamedString):
files = [file.name for file in files]
files_info = {}
for file in files:
transcribed_segments, time_for_task = self.run(
file,
progress,
file_format,
add_timestamp,
*pipeline_params,
)
file_name, file_ext = os.path.splitext(os.path.basename(file))
if save_same_dir and input_folder_path:
output_dir = os.path.dirname(file)
subtitle, file_path = generate_file(
output_dir=output_dir,
output_file_name=file_name,
output_format=file_format,
result=transcribed_segments,
add_timestamp=add_timestamp,
**writer_options
)
subtitle, file_path = generate_file(
output_dir=self.output_dir,
output_file_name=file_name,
output_format=file_format,
result=transcribed_segments,
add_timestamp=add_timestamp,
**writer_options
)
files_info[file_name] = {"subtitle": read_file(file_path), "time_for_task": time_for_task, "path": file_path}
total_result = ''
total_time = 0
for file_name, info in files_info.items():
total_result += '------------------------------------\n'
total_result += f'{file_name}\n\n'
total_result += f'{info["subtitle"]}'
total_time += info["time_for_task"]
result_str = f"Done in {self.format_time(total_time)}! Subtitle is in the outputs folder.\n\n{total_result}"
result_file_path = [info['path'] for info in files_info.values()]
return result_str, result_file_path
except Exception as e:
raise RuntimeError(f"Error transcribing file: {e}") from e
finally:
self.release_cuda_memory()
def transcribe_mic(self,
mic_audio: str,
file_format: str = "SRT",
add_timestamp: bool = True,
progress=gr.Progress(),
*pipeline_params,
) -> Tuple[str, str]:
"""
Write subtitle file from microphone
Parameters
----------
mic_audio: str
Audio file path from gr.Microphone()
file_format: str
Subtitle File format to write from gr.Dropdown(). Supported format: [SRT, WebVTT, txt]
add_timestamp: bool
Boolean value from gr.Checkbox() that determines whether to add a timestamp at the end of the filename.
progress: gr.Progress
Indicator to show progress directly in gradio.
*pipeline_params: tuple
Parameters related with whisper. This will be dealt with "WhisperParameters" data class
Returns
----------
result_str:
Result of transcription to return to gr.Textbox()
result_file_path:
Output file path to return to gr.Files()
"""
try:
params = TranscriptionPipelineParams.from_list(list(pipeline_params))
writer_options = {
"highlight_words": True if params.whisper.word_timestamps else False
}
progress(0, desc="Loading Audio..")
transcribed_segments, time_for_task = self.run(
mic_audio,
progress,
file_format,
add_timestamp,
*pipeline_params,
)
progress(1, desc="Completed!")
file_name = "Mic"
subtitle, file_path = generate_file(
output_dir=self.output_dir,
output_file_name=file_name,
output_format=file_format,
result=transcribed_segments,
add_timestamp=add_timestamp,
**writer_options
)
result_str = f"Done in {self.format_time(time_for_task)}! Subtitle file is in the outputs folder.\n\n{subtitle}"
return result_str, file_path
except Exception as e:
raise RuntimeError(f"Error transcribing mic: {e}") from e
finally:
self.release_cuda_memory()
def transcribe_youtube(self,
youtube_link: str,
file_format: str = "SRT",
add_timestamp: bool = True,
progress=gr.Progress(),
*pipeline_params,
) -> Tuple[str, str]:
"""
Write subtitle file from Youtube
Parameters
----------
youtube_link: str
URL of the Youtube video to transcribe from gr.Textbox()
file_format: str
Subtitle File format to write from gr.Dropdown(). Supported format: [SRT, WebVTT, txt]
add_timestamp: bool
Boolean value from gr.Checkbox() that determines whether to add a timestamp at the end of the filename.
progress: gr.Progress
Indicator to show progress directly in gradio.
*pipeline_params: tuple
Parameters related with whisper. This will be dealt with "WhisperParameters" data class
Returns
----------
result_str:
Result of transcription to return to gr.Textbox()
result_file_path:
Output file path to return to gr.Files()
"""
try:
params = TranscriptionPipelineParams.from_list(list(pipeline_params))
writer_options = {
"highlight_words": True if params.whisper.word_timestamps else False
}
progress(0, desc="Loading Audio from Youtube..")
yt = get_ytdata(youtube_link)
audio = get_ytaudio(yt)
transcribed_segments, time_for_task = self.run(
audio,
progress,
file_format,
add_timestamp,
*pipeline_params,
)
progress(1, desc="Completed!")
file_name = safe_filename(yt.title)
subtitle, file_path = generate_file(
output_dir=self.output_dir,
output_file_name=file_name,
output_format=file_format,
result=transcribed_segments,
add_timestamp=add_timestamp,
**writer_options
)
result_str = f"Done in {self.format_time(time_for_task)}! Subtitle file is in the outputs folder.\n\n{subtitle}"
if os.path.exists(audio):
os.remove(audio)
return result_str, file_path
except Exception as e:
raise RuntimeError(f"Error transcribing youtube: {e}") from e
finally:
self.release_cuda_memory()
def get_compute_type(self):
if "float16" in self.available_compute_types:
return "float16"
if "float32" in self.available_compute_types:
return "float32"
else:
return self.available_compute_types[0]
def get_available_compute_type(self):
if self.device == "cuda":
return list(ctranslate2.get_supported_compute_types("cuda"))
else:
return list(ctranslate2.get_supported_compute_types("cpu"))
def offload(self):
"""Offload the model and free up the memory"""
if self.model is not None:
del self.model
self.model = None
if self.device == "cuda":
self.release_cuda_memory()
gc.collect()
@staticmethod
def format_time(elapsed_time: float) -> str:
"""
Get {hours} {minutes} {seconds} time format string
Parameters
----------
elapsed_time: str
Elapsed time for transcription
Returns
----------
Time format string
"""
hours, rem = divmod(elapsed_time, 3600)
minutes, seconds = divmod(rem, 60)
time_str = ""
if hours:
time_str += f"{hours} hours "
if minutes:
time_str += f"{minutes} minutes "
seconds = round(seconds)
time_str += f"{seconds} seconds"
return time_str.strip()
@staticmethod
def get_device():
if torch.cuda.is_available():
return "cuda"
elif torch.backends.mps.is_available():
if not BaseTranscriptionPipeline.is_sparse_api_supported():
# Device `SparseMPS` is not supported for now. See : https://github.com/pytorch/pytorch/issues/87886
return "cpu"
return "mps"
else:
return "cpu"
@staticmethod
def is_sparse_api_supported():
if not torch.backends.mps.is_available():
return False
try:
device = torch.device("mps")
sparse_tensor = torch.sparse_coo_tensor(
indices=torch.tensor([[0, 1], [2, 3]]),
values=torch.tensor([1, 2]),
size=(4, 4),
device=device
)
return True
except RuntimeError:
return False
@staticmethod
def release_cuda_memory():
"""Release memory"""
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
@staticmethod
def remove_input_files(file_paths: List[str]):
"""Remove gradio cached files"""
if not file_paths:
return
for file_path in file_paths:
if file_path and os.path.exists(file_path):
os.remove(file_path)
@staticmethod
def validate_gradio_values(params: TranscriptionPipelineParams):
"""
Validate gradio specific values that can't be displayed as None in the UI.
Related issue : https://github.com/gradio-app/gradio/issues/8723
"""
if params.whisper.lang is None:
pass
elif params.whisper.lang == AUTOMATIC_DETECTION:
params.whisper.lang = None
else:
language_code_dict = {value: key for key, value in whisper.tokenizer.LANGUAGES.items()}
params.whisper.lang = language_code_dict[params.whisper.lang]
if params.whisper.initial_prompt == GRADIO_NONE_STR:
params.whisper.initial_prompt = None
if params.whisper.prefix == GRADIO_NONE_STR:
params.whisper.prefix = None
if params.whisper.hotwords == GRADIO_NONE_STR:
params.whisper.hotwords = None
if params.whisper.max_new_tokens == GRADIO_NONE_NUMBER_MIN:
params.whisper.max_new_tokens = None
if params.whisper.hallucination_silence_threshold == GRADIO_NONE_NUMBER_MIN:
params.whisper.hallucination_silence_threshold = None
if params.whisper.language_detection_threshold == GRADIO_NONE_NUMBER_MIN:
params.whisper.language_detection_threshold = None
if params.vad.max_speech_duration_s == GRADIO_NONE_NUMBER_MAX:
params.vad.max_speech_duration_s = float('inf')
return params
@staticmethod
def cache_parameters(
params: TranscriptionPipelineParams,
file_format: str = "SRT",
add_timestamp: bool = True
):
"""Cache parameters to the yaml file"""
cached_params = load_yaml(DEFAULT_PARAMETERS_CONFIG_PATH)
param_to_cache = params.to_dict()
cached_yaml = {**cached_params, **param_to_cache}
cached_yaml["whisper"]["add_timestamp"] = add_timestamp
cached_yaml["whisper"]["file_format"] = file_format
supress_token = cached_yaml["whisper"].get("suppress_tokens", None)
if supress_token and isinstance(supress_token, list):
cached_yaml["whisper"]["suppress_tokens"] = str(supress_token)
if cached_yaml["whisper"].get("lang", None) is None:
cached_yaml["whisper"]["lang"] = AUTOMATIC_DETECTION.unwrap()
else:
language_dict = whisper.tokenizer.LANGUAGES
cached_yaml["whisper"]["lang"] = language_dict[cached_yaml["whisper"]["lang"]]
if cached_yaml["vad"].get("max_speech_duration_s", float('inf')) == float('inf'):
cached_yaml["vad"]["max_speech_duration_s"] = GRADIO_NONE_NUMBER_MAX
if cached_yaml is not None and cached_yaml:
save_yaml(cached_yaml, DEFAULT_PARAMETERS_CONFIG_PATH)
@staticmethod
def resample_audio(audio: Union[str, np.ndarray],
new_sample_rate: int = 16000,
original_sample_rate: Optional[int] = None,) -> np.ndarray:
"""Resamples audio to 16k sample rate, standard on Whisper model"""
if isinstance(audio, str):
audio, original_sample_rate = torchaudio.load(audio)
else:
if original_sample_rate is None:
raise ValueError("original_sample_rate must be provided when audio is numpy array.")
audio = torch.from_numpy(audio)
resampler = torchaudio.transforms.Resample(orig_freq=original_sample_rate, new_freq=new_sample_rate)
resampled_audio = resampler(audio).numpy()
return resampled_audio
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