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unifyed documentation

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This commit is contained in:
Jaikinator
2023-08-23 13:17:13 +02:00
parent a21bc32f7d
commit d2c57866df
2 changed files with 173 additions and 113 deletions
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autotranscript/audio.py
+78 -59
View File
@@ -1,34 +1,63 @@
"""
Audio Processor Module
=======================
This module provides the AudioProcessor class, utilizing PyTorchaudio for handling audio files.
It includes functionalities to load, cut, and manage audio waveforms, offering efficient and
flexible audio processing.
Available Classes:
- AudioProcessor: Processes audio waveforms and provides methods for loading,
cutting, and handling audio.
Usage:
from .audio_import AudioProcessor
processor = AudioProcessor.from_file("path/to/audiofile.wav")
cut_waveform = processor.cut(start=1.0, end=5.0)
Constants:
- SAMPLE_RATE (int): Default sample rate for processing.
- NORMALIZATION_FACTOR (float): Normalization factor for audio waveform.
"""
from subprocess import CalledProcessError, run
import numpy as np
import torch
from subprocess import CalledProcessError, run
from typing import Union
SAMPLE_RATE = 16000
NORMALIZATION_FACTOR = 32768.0
class AudioProcessor:
"""
Audio Processor using PyTorchaudio instead of PyDub
Audio Processor class that leverages PyTorchaudio to provide functionalities
for loading, cutting, and handling audio waveforms.
Attributes:
waveform: torch.Tensor
The audio waveform tensor.
sr: int
The sample rate of the audio.
"""
def __init__(self, waveform: torch.Tensor, sr : int = SAMPLE_RATE,
*args, **kwargs) -> None:
"""
Initialise audio processor
:param waveform: waveform
:param sr: sample rate
:param args: additional arguments
:param kwargs: additional keyword arguments
example:
- device: device to use for processing
if cuda is available, cuda is used
Initialize the AudioProcessor object.
Args:
waveform (torch.Tensor): The audio waveform tensor.
sr (int, optional): The sample rate of the audio. Defaults to SAMPLE_RATE.
args: Additional arguments.
kwargs: Additional keyword arguments, e.g., device to use for processing.
If CUDA is available, it defaults to CUDA.
Raises:
ValueError: If the provided sample rate is not of type int.
"""
if "device" in kwargs:
device = kwargs["device"]
else:
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
device = kwargs.get("device", "cuda" if torch.cuda.is_available() else "cpu")
self.waveform = waveform.to(device)
self.sr = sr
@@ -40,9 +69,13 @@ class AudioProcessor:
@classmethod
def from_file(cls, file: str, *args, **kwargs) -> 'AudioProcessor':
"""
Load audio file
:param file: audio file
:return: AudioProcessor
Create an AudioProcessor instance from an audio file.
Args:
file (str): The audio file path.
Returns:
AudioProcessor: An instance of the AudioProcessor class containing the loaded audio.
"""
audio, sr = cls.load_audio(file , *args, **kwargs)
@@ -54,42 +87,37 @@ class AudioProcessor:
def cut(self, start: float, end: float) -> torch.Tensor:
"""
Cut audio file
:param start: start time in seconds
:param end: end time in seconds
:return: AudioProcessor
Cut a segment from the audio waveform between the specified start and end times.
Args:
start (float): Start time in seconds.
end (float): End time in seconds.
Returns:
torch.Tensor: The cut waveform segment.
"""
if isinstance(start, float):
start = torch.Tensor([start])
if isinstance(end, float):
end = torch.Tensor([end])
sr = torch.Tensor([self.sr])
start = int(start * sr)
end = torch.ceil(end * sr)
return self.waveform[start:end.to(int)]
start = int(start * self.sr)
end = int(torch.ceil(end * self.sr))
return self.waveform[start:end]
@staticmethod
def load_audio(file: str, sr: int = SAMPLE_RATE):
"""
Open an audio file and read as mono waveform, resampling as necessary
Open an audio file and read it as a mono waveform, resampling if necessary.
This method ensures compatibility with pyannote.audio
and requires the ffmpeg CLI in PATH.
Changed from original function at whisper.audio.load_audio to ensure
compatibility with pyannote.audio
Parameters
----------
file: str
The audio file to open
Args:
file (str): The audio file to open.
sr (int, optional): The desired sample rate. Defaults to SAMPLE_RATE.
sr: int
The sample rate to resample the audio if necessary
Returns:
tuple: A NumPy array containing the audio waveform in float32 dtype
and the sample rate.
Returns
-------
A NumPy array containing the audio waveform, in float32 dtype.
Raises:
RuntimeError: If failed to load audio.
"""
# This launches a subprocess to decode audio while down-mixing
# and resampling as necessary. Requires the ffmpeg CLI in PATH.
@@ -111,18 +139,9 @@ class AudioProcessor:
except CalledProcessError as e:
raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
out = np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
out = np.frombuffer(out, np.int16).flatten().astype(np.float32) / NORMALIZATION_FACTOR
return out , sr
def __repr__(self) -> str:
return f'TorchAudioProcessor(waveform={len(self.waveform)}, sr={int(self.sr)})'
def __str__(self) -> str:
return f'TorchAudioProcessor(waveform={len(self.waveform)}, sr={int(self.sr)})'
if __name__ == "__main__":
print("Testing AudioProcessor")
print(AudioProcessor.from_file("tests/test.wav"))
return f'TorchAudioProcessor(waveform={len(self.waveform)}, sr={int(self.sr)})'
autotranscript/diarisation.py
+95 -54
View File
@@ -1,7 +1,32 @@
"""
Diarisation class.
This class is used to diarize an audio file using a pretrained model
Diarisation Class
=================
This class serves as the heart of the speaker diarization system, responsible for identifying
and segmenting individual speakers from a given audio file. It leverages a pretrained model
from pyannote.audio, providing an accessible interface for audio processing tasks such as
speaker separation, and timestamping.
By encapsulating the complexities of the underlying model, it allows for straightforward
integration into various applications, ranging from transcription services to voice assistants.
Available Classes:
- Diariser: Main class for performing speaker diarization.
Includes methods for loading models, processing audio files,
and formatting the diarization output.
Constants:
- TOKEN_PATH (str): Path to the Pyannote token.
- PYANNOTE_DEFAULT_PATH (str): Default path to Pyannote models.
- PYANNOTE_DEFAULT_CONFIG (str): Default configuration for Pyannote models.
Usage:
from .diarisation import Diariser
model = Diariser.load_model(model="path/to/model/config.yaml")
diarisation_output = model.diarization("path/to/audiofile.wav")
"""
import os
from pathlib import Path
from typing import TypeVar, Union
@@ -10,7 +35,7 @@ from pyannote.audio import Pipeline
from pyannote.audio.pipelines.speaker_diarization import SpeakerDiarization
from torch import Tensor
from .misc import PYANNOTE_DEFAULT_CONFIG, PYANNOTE_DEFAULT_PATH
from .misc import PYANNOTE_DEFAULT_PATH, PYANNOTE_DEFAULT_CONFIG
Annotation = TypeVar('Annotation')
TOKEN_PATH = os.path.join(os.path.dirname(
@@ -18,11 +43,13 @@ TOKEN_PATH = os.path.join(os.path.dirname(
class Diariser:
"""
Diarisation class
This class is used to diarize an audio file using a pretrained model
from pyannote.audio.
:param model: model to use for diarization
Handles the diarization process of an audio file using a pretrained model
from pyannote.audio. Diarization is the task of determining "who spoke when."
Args:
model: The pretrained model to use for diarization.
"""
def __init__(self, model) -> None:
self.model = model
@@ -30,11 +57,20 @@ class Diariser:
def diarization(self, audiofile : Union[str, Tensor, dict] ,
*args, **kwargs) -> Annotation:
"""
Diarization of audio file
:param audiofile: path to audio file or torch.Tensor
:param args: args for diarization model
:param kwargs: kwargs for diarization model
:return: diarization
Perform speaker diarization on the provided audio file,
effectively separating different speakers
and providing a timestamp for each segment.
Args:
audiofile: The path to the audio file or a torch.Tensor
containing the audio data.
args: Additional arguments for the diarization model.
kwargs: Additional keyword arguments for the diarization model.
Returns:
dict: A dictionary containing speaker names,
segments, and other information related
to the diarization process.
"""
kwargs = self._get_diarisation_kwargs(**kwargs)
@@ -47,10 +83,14 @@ class Diariser:
@staticmethod
def format_diarization_output(dia : Annotation) -> dict:
"""
Format diarization output to a list of tuples
:param dia: diarization output
:return: dict with speaker names as keys and list of tuples
as values and list of different speakers
Formats the raw diarization output into a more usable structure for this project.
Args:
dia: Raw diarization output.
Returns:
dict: A structured representation of the diarization, with speaker names
as keys and a list of tuples representing segments as values.
"""
dia_list = list(dia.itertracks(yield_label=True))
@@ -103,10 +143,14 @@ class Diariser:
@staticmethod
def _get_token():
"""
Get token from .pyannotetoken.txt
:raises ValueError: No token found
:return: Huggingface token
:rtype: str
Retrieves the Huggingface token from a local file. This token is required
for accessing certain online resources.
Raises:
ValueError: If the token is not found.
Returns:
str: The Huggingface token.
"""
if os.path.exists(TOKEN_PATH):
@@ -121,12 +165,13 @@ class Diariser:
@staticmethod
def _save_token(token):
"""
Save token to .pyannotetoken.txt
Saves the provided Huggingface token to a local file. This facilitates future
access to online resources without needing to repeatedly authenticate.
:param token: Huggingface token
:type token: str
Args:
token: The Huggingface token to save.
"""
with open(TOKEN_PATH, 'r', encoding="utf-8") as file:
with open(TOKEN_PATH, 'w', encoding="utf-8") as file:
file.write(token)
@classmethod
@@ -137,22 +182,21 @@ class Diariser:
cache_dir: Union[Path, str] = PYANNOTE_DEFAULT_PATH,
hparams_file: Union[str, Path] = None
) -> Pipeline:
"""
Load modules from pyannote
Parameters
----------
model : str
pyannote model
default: /models/pyannote/speaker_diarization/config.yaml
token : str
HUGGINGFACE_TOKEN
local : bool
If true, load from local cache
Returns
-------
Pipeline Object
"""
Loads a pretrained model from pyannote.audio,
either from a local cache or online repository.
Args:
model: Path or identifier for the pyannote model.
default: /models/pyannote/speaker_diarization/config.yaml
token: Optional HUGGINGFACE_TOKEN for authenticated access.
cache_token: Whether to cache the token locally for future use.
cache_dir: Directory for caching models.
hparams_file: Path to a YAML file containing hyperparameters.
Returns:
Pipeline: A pyannote.audio Pipeline object, encapsulating the loaded model.
"""
if cache_token and token is not None:
@@ -161,38 +205,35 @@ class Diariser:
if not os.path.exists(model) and token is None:
token = cls._get_token()
model = 'pyannote/speaker-diarization'
_model = Pipeline.from_pretrained(model,
use_auth_token = token,
cache_dir = cache_dir,
hparams_file = hparams_file,)
if model is None:
if _model is None:
raise ValueError('Unable to load model either from local cache' \
'or from huggingface.co models. Please check your token' \
'or your local model path')
return cls(_model)
@staticmethod
def _get_diarisation_kwargs(**kwargs) -> dict:
"""
Get kwargs for pyannote diarization model
Ensure that kwargs are valid
:return: kwargs for pyannote diarization model
:rtype: dict
Validates and extracts the keyword arguments for the pyannote diarization model.
Ensures that the provided keyword arguments match the expected parameters,
filtering out any invalid or unnecessary arguments.
Returns:
dict: A dictionary containing the validated keyword arguments.
"""
_possible_kwargs = SpeakerDiarization.apply.__code__.co_varnames
diarisation_kwargs = dict()
for k in kwargs.keys():
if k in _possible_kwargs:
diarisation_kwargs[k] = kwargs[k]
diarisation_kwargs = {k: v for k, v in kwargs.items() if k in _possible_kwargs}
return diarisation_kwargs
def __repr__(self):
return f"Diarisation(model={self.model})"
def __str__(self):
return f"Diarisation(model={self.model})"