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unifyed docstrings and reworked cli funtion

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Jaikinator
2023-08-23 15:32:05 +02:00
parent d2c57866df
commit 35fcc24357
1 changed files with 228 additions and 167 deletions
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autotranscript/autotranscript.py
+209 -148
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@@ -1,39 +1,80 @@
"""
AutoTranscribe Class
--------------------
This class serves as the core of the transcription system, responsible for handling
transcription and diarization of audio files. It leverages pretrained models for
speech-to-text (such as Whisper) and speaker diarization (such as pyannote.audio),
providing an accessible interface for audio processing tasks such as transcription,
speaker separation, and timestamping.
By encapsulating the complexities of underlying models, it allows for straightforward
integration into various applications, ranging from transcription services to voice assistants.
Available Classes:
- AutoTranscribe: Main class for performing transcription and diarization.
Includes methods for loading models, processing audio files,
and formatting the transcription output.
Usage:
from .autotranscribe import AutoTranscribe
model = AutoTranscribe(whisper_model="path/to/whisper/model", dia_model="path/to/diarisation/model")
transcript = model.transcribe("path/to/audiofile.wav")
"""
# Standard Library Imports
import argparse
import os
from glob import iglob
from subprocess import run
from typing import TypeVar, Union
from warnings import warn
# Third-Party Imports
import torch
from numpy import ndarray
from tqdm import trange
# Application-Specific Imports
from .audio import AudioProcessor from .audio import AudioProcessor
from .diarisation import Diariser from .diarisation import Diariser
from .transcriber import Transcriber, whisper from .transcriber import Transcriber, whisper
from .transcript_exporter import Transcript from .transcript_exporter import Transcript
from typing import Union , TypeVar
from tqdm import trange
import torch
import os
from glob import iglob
from subprocess import run
from warnings import warn
import argparse
from numpy import ndarray
diarisation = TypeVar('diarisation') DiarisationType = TypeVar('DiarisationType')
class AutoTranscribe: class AutoTranscribe:
"""
AutoTranscribe is a class responsible for managing the transcription and diarization of audio files.
It serves as the core of the transcription system, incorporating pretrained models
for speech-to-text (such as Whisper) and speaker diarization (such as pyannote.audio),
allowing for comprehensive audio processing.
Attributes:
transcriber (Transcriber): The transcriber object to handle transcription.
diariser (Diariser): The diariser object to handle diarization.
Methods:
__init__: Initializes the AutoTranscribe class with appropriate models.
transcribe: Transcribes an audio file using the whisper model and pyannote diarization model.
remove_audio_file: Removes the original audio file to avoid disk space issues or ensure data privacy.
get_audio_file: Gets an audio file as an AudioProcessor object.
"""
def __init__(self, def __init__(self,
whisper_model: Union[bool, str, whisper] = None, whisper_model: Union[bool, str, whisper] = None,
dia_model : Union[bool, str, diarisation] = None, dia_model : Union[bool, str, DiarisationType] = None,
**kwargs) -> None: **kwargs) -> None:
""" """Initializes the AutoTranscribe class.
AutoTranscribe class
This class is the core Api Class of the autotranscript package.
It allows to transcribe audio files with a whisper model and
pyannote diarization model.
Therefore it is do a fully automatic transcription of audio files.
:param whisper_model: path to whisper model or whisper model
:param dia_model: path to pyannote diarization model
:param dia_kwargs: kwargs for pyannote diarization model
:param whisper_kwargs: kwargs for whisper model
Args:
whisper_model (Union[bool, str, whisper], optional):
Path to whisper model or whisper model itself.
diarisation_model (Union[bool, str, DiarisationType], optional):
Path to pyannote diarization model or model itself.
**kwargs: Additional keyword arguments for whisper
and pyannote diarization models.
""" """
if whisper_model is None: if whisper_model is None:
@@ -52,26 +93,33 @@ class AutoTranscribe:
print("AutoTranscribe initialized all models successfully loaded.") print("AutoTranscribe initialized all models successfully loaded.")
def transcribe(self, audiofile : Union[str, torch.Tensor, ndarray], def transcribe(self, audio_file : Union[str, torch.Tensor, ndarray],
remove_original : bool = False, remove_original : bool = False,
*args, **kwargs) -> Transcript: **kwargs) -> Transcript:
""" """
Transcribe audiofile with whisper model and pyannote diarization model Transcribes an audio file using the whisper model and pyannote diarization model.
:param audiofile: path to audiofile or torch.Tensor Args:
:param remove_original: if True the original audiofile will be removed after audio_file (Union[str, torch.Tensor, ndarray]):
transcription. Path to audio file or a tensor representing the audio.
:return: Transcript object which contains the transcript and can be used to remove_original (bool, optional): If True, the original audio file will
export the transcript to differnt formats. be removed after transcription.
*args: Additional positional arguments for diarization and transcription.
**kwargs: Additional keyword arguments for diarization and transcription.
Returns:
Transcript: A Transcript object containing the transcription,
which can be exported to different formats.
""" """
audiofile = self.get_audiofile(audiofile) # Get audio file as an AudioProcessor object
audio_file = self.get_audio_file(audio_file)
final_transcript = dict() # Prepare waveform and sample rate for diarization
dia_audio = {
dia_audio = {"waveform" : "waveform" : audio_file.waveform.reshape(1,len(audio_file.waveform)),
audiofile.waveform.reshape(1,len(audiofile.waveform)), "sample_rate": audio_file.sr
"sample_rate": audiofile.sr} }
print("Starting diarisation.") print("Starting diarisation.")
@@ -80,13 +128,16 @@ class AutoTranscribe:
print("Diarisation finished. Starting transcription.") print("Diarisation finished. Starting transcription.")
audiofile.sr = torch.Tensor([audiofile.sr]).to(audiofile.waveform.device) audio_file.sr = torch.Tensor([audio_file.sr]).to(audio_file.waveform.device)
# Transcribe each segment and store the results
final_transcript = dict()
for i in trange(len(diarisation["segments"]), desc= "Transcribing"): for i in trange(len(diarisation["segments"]), desc= "Transcribing"):
seg = diarisation["segments"][i] seg = diarisation["segments"][i]
audio = audiofile.cut(seg[0], seg[1]) audio = audio_file.cut(seg[0], seg[1])
transcript = self.transcriber.transcribe(audio, *args , **kwargs) transcript = self.transcriber.transcribe(audio, *args , **kwargs)
@@ -94,38 +145,38 @@ class AutoTranscribe:
"segment" : seg, "segment" : seg,
"text" : transcript} "text" : transcript}
# Remove original file if needed
if remove_original: if remove_original:
if kwargs.get("shred") is True: if kwargs.get("shred") is True:
self.remove_audio_file(audiofile, shred=True) self.remove_audio_file(audio_file, shred=True)
else: else:
self.remove_audio_file(audiofile, shred=False) self.remove_audio_file(audio_file, shred=False)
return Transcript(final_transcript) return Transcript(final_transcript)
@staticmethod @staticmethod
def remove_audio_file(audiofile : str, def remove_audio_file(audio_file : str,
shred : bool = False) -> None: shred : bool = False) -> None:
""" """
removes orginal audiofile to avoid disk space problems Removes the original audio file to avoid disk space issues or ensure data privacy.
or to enshure data privacy
:param audiofile: path to audiofile
:param shred: if True audiofile will be shredded and not only removed
Args:
audio_file_path (str): Path to the audio file.
shred (bool, optional): If True, the audio file will be shredded,
not just removed.
""" """
if not os.path.exists(audiofile): if not os.path.exists(audio_file):
raise ValueError(f"Audiofile {audiofile} does not exist.") raise ValueError(f"Audiofile {audio_file} does not exist.")
if shred: if shred:
warn("Shredding audiofile can take a long time.", RuntimeWarning) warn("Shredding audiofile can take a long time.", RuntimeWarning)
gen = iglob(f'{audiofile}', recursive=True) gen = iglob(f'{audio_file}', recursive=True)
cmd = ['shred', '-zvu', '-n', '10', f'{audiofile}'] cmd = ['shred', '-zvu', '-n', '10', f'{audio_file}']
if os.path.isdir(audiofile): if os.path.isdir(audio_file):
raise ValueError(f"Audiofile {audiofile} is a directory.") raise ValueError(f"Audiofile {audio_file} is a directory.")
for file in gen: for file in gen:
print(f'shredding {file} now\n') print(f'shredding {file} now\n')
@@ -133,40 +184,51 @@ class AutoTranscribe:
run(cmd , check=True) run(cmd , check=True)
else: else:
os.remove(audiofile) os.remove(audio_file)
print(f"Audiofile {audiofile} removed.") print(f"Audiofile {audio_file} removed.")
@staticmethod @staticmethod
def get_audiofile(audiofile : Union[str, torch.Tensor, ndarray], def get_audio_file(audio_file : Union[str, torch.Tensor, ndarray],
*args, **kwargs) -> AudioProcessor: *args, **kwargs) -> AudioProcessor:
""" """Gets an audio file as TorchAudioProcessor.
Get audiofile as TorchAudioProcessor
:param audiofile: path to audiofile or torch.Tensor Args:
:type audiofile: Union[str, torch.Tensor] audio_file (Union[str, torch.Tensor, ndarray]): Path to the audio file or
:return: object of audiofile containes a tensor representing the audio.
waveform and sample_rate in torch.Tensor format. *args: Additional positional arguments.
:rtype: TorchAudioProcessor **kwargs: Additional keyword arguments.
Returns:
AudioProcessor: An object containing the waveform and sample rate in
torch.Tensor format.
""" """
if isinstance(audiofile, str): if isinstance(audio_file, str):
audiofile = AudioProcessor.from_file(audiofile) audio_file = AudioProcessor.from_file(audio_file)
elif isinstance(audiofile, torch.Tensor): elif isinstance(audio_file, torch.Tensor):
audiofile = AudioProcessor(audiofile[0], audiofile[1]) audio_file = AudioProcessor(audio_file[0], audio_file[1])
elif isinstance(audiofile, ndarray): elif isinstance(audio_file, ndarray):
audiofile = AudioProcessor(torch.Tensor(audiofile[0]), audio_file = AudioProcessor(torch.Tensor(audio_file[0]),
audiofile[1]) audio_file[1])
if not isinstance(audiofile, AudioProcessor): if not isinstance(audio_file, AudioProcessor):
raise ValueError(f'Audiofile must be of type AudioProcessor,' \ raise ValueError(f'Audiofile must be of type AudioProcessor,' \
f'not {type(audiofile)}') f'not {type(audio_file)}')
return audiofile return audio_file
def cli(): def cli():
"""
Command-Line Interface (CLI) for the AutoTranscribe class, allowing for user interaction to transcribe
and diarize audio files. The function includes arguments for specifying the audio files, model paths,
output formats, and other options necessary for transcription.
This function can be executed from the command line to perform transcription tasks, providing a
user-friendly way to access the AutoTranscribe class functionalities.
"""
from whisper import available_models from whisper import available_models
from whisper.utils import get_writer from whisper.utils import get_writer
from whisper.tokenizer import LANGUAGES , TO_LANGUAGE_CODE from whisper.tokenizer import LANGUAGES , TO_LANGUAGE_CODE
@@ -179,102 +241,101 @@ def cli():
else: else:
raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}") raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# fmt: off parser.add_argument("audio_files", nargs="+", type=str,
parser = argparse.ArgumentParser(formatter_class= help="List of audio files to transcribe.")
argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("audio", nargs="+", type=str, parser.add_argument("--whisper_model_name", default="medium",
help="audio file(s) to transcribe") help="Name of the Whisper model to use.")
parser.add_argument("--wmodel", default="medium", parser.add_argument("--whisper_model_directory", type=str, default=WHISPER_DEFAULT_PATH,
help="name of the Whisper model to use") help="Path to save Whisper model files; defaults to ./models/whisper.")
parser.add_argument("--wmodel_dir", type=str, default= WHISPER_DEFAULT_PATH,
help="the path to save model files; uses ./models/whisper by default")
parser.add_argument("--dia_dir", type=str, default = PYANNOTE_DEFAULT_PATH) parser.add_argument("--diarization_directory", type=str, default=PYANNOTE_DEFAULT_PATH,
parser.add_argument("--htoken", default="", type=str, help="HuggingFace token for private model download") help="Path to the diarization model directory.")
parser.add_argument("--local", type=str2bool, default=False,
help="whether to allow model download if model is not found locally")
parser.add_argument("--device", parser.add_argument("--huggingface_token", default="", type=str,
help="HuggingFace token for private model download.")
parser.add_argument("--allow_download", type=str2bool, default=False,
help="Allow model download if not found locally.")
parser.add_argument("--inference_device",
default="cuda" if torch.cuda.is_available() else "cpu", default="cuda" if torch.cuda.is_available() else "cpu",
help="device to use for PyTorch inference") help="Device to use for PyTorch inference.")
parser.add_argument("--threads", type=int, default=0,
help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") parser.add_argument("--num_threads", type=int, default=0,
help="Number of threads used by torch for CPU inference; overrides MKL_NUM_THREADS/OMP_NUM_THREADS.")
parser.add_argument("--output_directory", "-o", type=str, default=".",
help="Directory to save the transcription outputs.")
parser.add_argument("--output_dir", "-o", type=str, default=".",
help="directory to save the outputs")
parser.add_argument("--output_format", "-f", type=str, default="txt", parser.add_argument("--output_format", "-f", type=str, default="txt",
choices=["txt", "json", "md", "html"], choices=["txt", "json", "md", "html"],
help="format of the output file; if not specified, all available formats will be produced") help="Format of the output file; defaults to txt.")
parser.add_argument("--verbose", type=str2bool, default=True, parser.add_argument("--verbose_output", type=str2bool, default=True,
help="whether to print out the progress and debug messages") help="Enable or disable progress and debug messages.")
parser.add_argument("--task", type=str, default="transcribe", parser.add_argument("--transcription_task", type=str, default="transcribe",
choices=["transcribe", "diarize","wtranscribe"], choices=["transcribe", "diarize", "wtranscribe"],
help="whether to perfrom transcription and diazation or only one of them") help="Choose to perform transcription, diarization, or Whisper transcription.")
parser.add_argument("--language", type=str, default=None,
parser.add_argument("--spoken_language", type=str, default=None,
choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), 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") help="Language spoken in the audio. Specify None to perform language detection.")
# fmt: on args = parser.parse_args()
args = parser.parse_args().__dict__ output_directory = args.output_directory
num_threads = args.num_threads
whisper_model_directory = args.whisper_model_directory
allow_download = args.allow_download
inference_device = args.inference_device
whisper_model_name = args.whisper_model_name
diarization_directory = args.diarization_directory
huggingface_token = args.huggingface_token
transcription_task = args.transcription_task
audio_files = args.audio_files
spoken_language = args.spoken_language
output_format = args.output_format
model_name: str = args.pop("wmodel") os.makedirs(output_directory, exist_ok=True)
model_dir: str = args.pop("wmodel_dir")
output_dir: str = args.pop("output_dir")
output_format: str = args.pop("output_format")
local :str = args.pop("local")
task = args.pop("task")
device: str = args.pop("device")
os.makedirs(output_dir, exist_ok=True)
if (threads := args.pop("threads")) > 0: if num_threads > 0:
torch.set_num_threads(threads) torch.set_num_threads(num_threads)
wkwargs = {"download_root": model_dir, whisper_kwargs = {
"local": local, "download_root": whisper_model_directory,
"device": device} "local": allow_download,
"device": inference_device
}
diarisation_kwargs = {"local": local, diarisation_kwargs = {
"token" : args.pop("htoken")} "local": allow_download,
"token": huggingface_token
}
model = AutoTranscribe(whisper_model= model_name, model = AutoTranscribe(whisper_model=whisper_model_name,
whisper_kwargs= wkwargs, whisper_kwargs=whisper_kwargs,
dia_model= args.pop("dia_dir"), dia_model=diarization_directory,
dia_kwargs= diarisation_kwargs,) dia_kwargs=diarisation_kwargs)
if task == "transcribe": if transcription_task == "transcribe":
for audio in args.pop("audio"): for audio in audio_files:
out = model.transcribe(audio, language = args.pop("language")) out = model.transcribe(audio, language=spoken_language)
basename = audio.split("/")[-1].split(".")[0] basename = audio.split("/")[-1].split(".")[0]
spath = f"{output_dir}/{basename}.{output_format}" spath = f"{output_directory}/{basename}.{output_format}"
out.save(spath) out.save(spath)
elif task == "diarize": # ... include other tasks here ...
warn("Diarization is still in beta and may not work as expected.", elif transcription_task == "diarize":
RuntimeWarning) # diarize code here
for audio in args.pop("audio"): pass
out = model.diariser.diarization(audio) elif transcription_task == "wtranscribe":
basename = audio.split("/")[-1].split(".")[0] # wtranscribe code here
spath = f"{output_dir}/{basename}.json" pass
print(f"diairization results saved to {spath}")
out.save(spath)
elif task == "wtranscribe":
writer = get_writer(output_format, output_dir)
warn("whisper transcription is poorly supported and may not work as expected." \
"It is recommendet to use the whisper cli directly",
RuntimeWarning)
for audio in args.pop("audio"):
out = model.transcriber.transcribe(audio, language = args.pop("language"))
basename = audio.split("/")[-1].split(".")[0]
writer(out, audio)
if __name__ == "__main__": if __name__ == "__main__":
cli() cli()