Fine-tuned version of nvidia/parakeet-tdt-0.6b-v3 on LibriSpeech train-clean-100, using NVIDIA NeMo. Fine-tuned with a warm start from the base model, a low learning rate (1e-4), and learning-rate warmup.

Results

Evaluated on LibriSpeech test-clean (2620 utterances):

Metric	Value
WER (raw)	2.03%
WER (normalized)	2.00%
Word accuracy	98.00%

Raw and normalized WER are nearly identical, indicating the model transcribes accurately including punctuation and casing (no formatting inflation).

Model description

Base model: nvidia/parakeet-tdt-0.6b-v3 (FastConformer encoder + TDT decoder, ~600M params)
Architecture: EncDecRNNTBPEModel (Token-and-Duration Transducer)
Language: English
Framework: NVIDIA NeMo

How to use

python

import nemo.collections.asr as nemo_asr
model = nemo_asr.models.ASRModel.from_pretrained("bhaveshsoni0023/parakeet-tdt-0.6b-librispeech100-ft")
print(model.transcribe(["audio.wav"])[0].text)   # 16 kHz mono

Training details

Data: LibriSpeech train-clean-100 (~100 hours), dev-clean for validation
Base tokenizer kept (no tokenizer change)
Optimizer: AdamW, lr 1e-4, warmup 2000 steps
Precision: bf16, batch size 32, 20 epochs
Audio: 16 kHz mono

License and attribution

Released under CC-BY-4.0, inherited from the base model and LibriSpeech. Base model: nvidia/parakeet-tdt-0.6b-v3 (NVIDIA), CC-BY-4.0. Attribution required. Data: LibriSpeech (Panayotov et al., 2015), CC-BY-4.0. Framework: NVIDIA NeMo — https://github.com/NVIDIA-NeMo/Speech