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SpeechBrain — PyTorch speech toolkit. ASR, speaker recognition, speech separation, diarization, enhancement, language identification, and TTS. Recipe-based training with pre-trained model zoo.
Passed 18 of 22 test cases with the skill, and 13 of 22 without it.
and 3 more in the full report
5 fixed13 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
When initializing SpeakerRecognition in SpeechBrain with from_hparams, what local savedir folder path is conventionally specified in the official snippet?
| Checked | Without skill | With skill |
|---|---|---|
| Uses pretrained_models/spkrec as the savedir parameter value. | Missed | Passed |
The instructions your model reads when the skill loads.
SpeechBrain is an open-source PyTorch speech processing toolkit covering ASR (speech-to-text), speaker recognition, speech separation, diarization, enhancement, language identification, emotion recognition, and text-to-speech. Provides pretrained models and recipe-based training.
uv pip install speechbrain
from speechbrain.inference.ASR import EncoderDecoderASR
asr_model = EncoderDecoderASR.from_hparams(
source="speechbrain/asr-crdnn-rnnlm-librispeech",
savedir="pretrained_models/asr")
transcript = asr_model.transcribe_file("audio.wav")
print(f"Transcript: {transcript}")
from speechbrain.inference.speaker import SpeakerRecognition
verification = SpeakerRecognition.from_hparams(
source="speechbrain/spkrec-ecapa-voxceleb",
savedir="pretrained_models/spkrec")
score, prediction = verification.verify_files("speaker1.wav", "speaker2.wav")
print(f"Same speaker: {prediction} (score: {score:.3f})")
test cases passed, vs 13 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.