hearing.backends.whisperx.adapter

Adapter for the WhisperX backend (transcribe -> align -> optionally diarize).

Pipeline, on a float32 mono 16 kHz array (no ffmpeg needed):

  1. whisperx.load_model(model, device, compute_type=...) (cached) then .transcribe(audio, batch_size=..., language=...);

  2. if align: whisperx.load_align_model + whisperx.align for accurate word timestamps;

  3. if diarize: pyannote via WhisperX’s DiarizationPipeline + whisperx.assign_word_speakers (needs a Hugging Face token, env HF_TOKEN).

The result is openai-whisper-shaped, mapped by hearing.backends.whisper_result_to_segments(). whisperx (and torch, for device auto-detection) are imported on first use.

Module Attributes

DEFAULT_COMPUTE_TYPES

compute_type used when none is given, by device.

Classes

Adapter(config)

WhisperX adapter (caches ASR, alignment and diarization models).
class hearing.backends.whisperx.adapter.Adapter(config)[source]

Bases: BaseTranscriberAdapter

WhisperX adapter (caches ASR, alignment and diarization models).

module_name = 'whisperx'

Engine module name — the seam tests use to inject a fake backend.

hearing.backends.whisperx.adapter.DEFAULT_COMPUTE_TYPES = {'cpu': 'int8', 'cuda': 'float16'}

compute_type used when none is given, by device.