Instructions to use ozonetg/parakeet-tdt-ctc-110m-recordings-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use ozonetg/parakeet-tdt-ctc-110m-recordings-adapter with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("ozonetg/parakeet-tdt-ctc-110m-recordings-adapter") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Parakeet-TDT-CTC-110M β "recordings" LinearAdapter (experimental)
A LinearAdapter (2.25M params, dim=128) on frozen
nvidia/parakeet-tdt_ctc-110m,
fine-tuned on a small private set of English telephone call recordings.
Adapter-only (~9.5 MB); base weights are not modified or redistributed.
Results β honest framing
| set | base WER | + adapter |
|---|---|---|
| in-domain recordings test | 40.42% | 38.81% (β1.61) |
This adapter gives only a marginal in-domain gain and regresses off-domain β it is published for completeness/reproducibility, not as a strong model. For a solid telephony adapter from the same project, see the Switchboard adapter (7.0% vs 15.98% WER). Best val WER 0.4723.
Usage (NeMo)
import nemo.collections.asr as nemo_asr
m = nemo_asr.models.ASRModel.from_pretrained("nvidia/parakeet-tdt_ctc-110m")
m.load_adapters("recordings_adapter.nemo")
m.set_enabled_adapters("recordings", enabled=True)
print(m.transcribe(["call.wav"]))
Training
LinearAdapter dim=128 (in_features=512, swish, pre-norm), bf16-mixed,
CosineAnnealing. Trained on private in-house telephone recordings (data not
released). Base nvidia/parakeet-tdt_ctc-110m (CC-BY-4.0) β attribution
required; this repo ships only the adapter delta.
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Model tree for ozonetg/parakeet-tdt-ctc-110m-recordings-adapter
Base model
nvidia/parakeet-tdt_ctc-110m