pipelines.pipeline

pipelines.pipeline(
    vad_model,
    emissions_model,
    transcription_model,
    audio_paths,
    audio_dir,
    backend='ct2',
    speeches=None,
    sample_rate=16000,
    chunk_size=30,
    alignment_strategy='chunk',
    text_normalizer_fn=text_normalizer,
    tokenizer=None,
    language=None,
    task='transcribe',
    beam_size=5,
    max_length=250,
    repetition_penalty=1.0,
    length_penalty=1.0,
    patience=1.0,
    no_repeat_ngram_size=0,
    start_wildcard=False,
    end_wildcard=False,
    blank_id=None,
    word_boundary=None,
    indent=2,
    ndigits=5,
    batch_size_files=1,
    num_workers_files=2,
    prefetch_factor_files=2,
    batch_size_features=8,
    num_workers_features=4,
    streaming=True,
    save_json=True,
    save_msgpack=False,
    save_emissions=True,
    return_alignments=False,
    delete_emissions=False,
    output_vad_dir='output/vad',
    output_transcriptions_dir='output/transcriptions',
    output_emissions_dir='output/emissions',
    output_alignments_dir='output/alignments',
    cache_dir='models',
    hf_token=None,
    device='cuda',
)

Run the full transcription pipeline (VAD -> Transcribe -> Emissions -> Align).

Parameters

Name	Type	Description	Default
vad_model	str	Voice Activity Detection model: “pyannote” or “silero”.	required
emissions_model	str	Hugging Face model ID for the emissions model (“org_name/model_name”).	required
transcription_model	str	Path to Hugging Face model ID for the transcription model (“org_name/model_name”).	required
audio_paths	list	List of audio file paths.	required
audio_dir	str	Directory containing audio files.	required
speeches	list[list[SpeechSegment]]	Existing speech segments for alignment.	None
backend	str	Backend to use for the transcription model: “ct2” or “hf”. Default is “ct2”.	'ct2'
sample_rate	int	Sample rate.	16000
chunk_size	int	Chunk size in seconds.	30
alignment_strategy	str	Alignment strategy (‘speech’ or ‘chunk’).	'chunk'
text_normalizer_fn	callable	Function to normalize text before forced alignment.	text_normalizer
tokenizer	object	An nltk tokenizer or a custom callable tokenizer that takes a string as input and returns a list of tuples (start_char, end_char), marking the spans/boundaries of sentences, paragraphs, or any other text unit of interest.	None
beam_size	int	Number of beams for beam search. Recommended: 5 for ct2 and 1 for hf (beam search is slow in Hugging Face transformers).	5
patience	float	Patience. Only implemented in ct2.	1.0
length_penalty	float	Length penalty for beam search. See HF source code for details	1.0
repetition_penalty	float	See HF source code for details.	1.0
max_length	int	Maximum length of generated text.	250
start_wildcard	bool	Add start wildcard to forced alignment.	False
end_wildcard	bool	Add end wildcard to forced alignment.	False
blank_id	int | None	Blank token ID of the emissions model (generally the pad token ID).	None
word_boundary	str | None	Word boundary character of the emissions model (usually “|”).	None
indent	int	JSON indentation.	2
ndigits	int	Number of digits for rounding.	5
batch_size_files	int	Batch size for files. Recommended to set to 1.	1
num_workers_files	int	Number of workers for file loading.	2
prefetch_factor_files	int	Prefetch factor for files.	2
batch_size_features	int	Batch size for feature extraction.	8
num_workers_features	int	Number of workers for feature extraction.	4
streaming	bool	Use streaming mode.	True
save_json	bool	Save results to JSON.	True
save_msgpack	bool	Save results to MessagePack.	False
save_emissions	bool	Save emissions.	True
return_alignments	bool	Return alignment results.	False
delete_emissions	bool	Whether to delete emissions numpy files after processing.	False
output_vad_dir	str	Output directory for VAD.	'output/vad'
output_transcriptions_dir	str	Output directory for transcriptions.	'output/transcriptions'
output_emissions_dir	str	Output directory for emissions.	'output/emissions'
output_alignments_dir	str	Output directory for alignments.	'output/alignments'
cache_dir	str	Cache directory for transcription and emissions models.	'models'
hf_token	str or None	Hugging Face authentication token for gated models.	None
device	str	Device to run models on. Default is cuda.	'cuda'

Examples

from pathlib import Path
from easyaligner.text import load_tokenizer
from easytranscriber.pipelines import pipeline
from easytranscriber.text.normalization import text_normalizer

tokenizer = load_tokenizer("english")
audio_files = [file.name for file in Path("data/en").glob("*.wav")]
pipeline(
    vad_model="pyannote",
    emissions_model="facebook/wav2vec2-base-960h",
    transcription_model="distil-whisper/distil-large-v3.5",
    audio_paths=audio_files,
    audio_dir="data/en",
    backend="ct2",
    language="en", # None to perform language detection
    tokenizer=tokenizer,
    text_normalizer_fn=text_normalizer,
    cache_dir="models",
)

Returns

Name	Type	Description
	list[list[SpeechSegment]] or None	If return_alignments is True, returns a list of alignment mappings for each audio file. Otherwise, returns None (the alignments are saved to disk only).