parakeet-tdt-0.6b-an4
This is a fine-tuned version of NVIDIA's nvidia/parakeet-tdt-0.6b-v3 automatic speech recognition (ASR) model, adapted to the AN4 dataset using the NVIDIA NeMo framework.
The base model is a 600M-parameter FastConformer model with a Token-and-Duration Transducer (TDT) decoder. This fine-tune adapts it to AN4's specific output style — lowercase text, no punctuation, and spelled-out letters and digit strings.
Model description
- Base model:
nvidia/parakeet-tdt-0.6b-v3(FastConformer encoder + TDT decoder) - Architecture:
EncDecRNNTBPEModel(RNN-Transducer with BPE tokenization) - Fine-tuned on: AN4 (English, spelled names / addresses / digit strings)
- Language: English
- Framework: NVIDIA NeMo
Intended use and limitations
Intended use. This model is a learning/demonstration artifact showing ASR fine-tuning on a small, domain-specific dataset. It transcribes short English utterances in AN4's style (spelled letters, spoken digits, short commands).
Important limitations — read before using:
- Highly specialized. It was fine-tuned only on AN4, which contains spelled names, addresses, dates, and digit strings — not natural sentences. It performs well on AN4-style speech and will behave poorly on general conversational or read speech.
- Output style. It emits lowercase text with no punctuation or capitalization, because that is AN4's label format. It does not restore casing or punctuation like the base model does.
- Not production-grade. AN4 is a tiny dataset (948 training utterances). This model is for education, not deployment.
- For general English ASR, use the base model
nvidia/parakeet-tdt-0.6b-v3instead.
How to use
Install NeMo, then load and transcribe with the NeMo toolkit:
```python import nemo.collections.asr as nemo_asr
model = nemo_asr.models.ASRModel.from_pretrained("bhaveshsoni0023/parakeet-tdt-0.6b-an4")
Audio must be 16 kHz mono WAV/FLAC
hyps = model.transcribe(["path/to/audio.wav"]) for h in hyps: print(h.text if hasattr(h, "text") else h) ```
Training details
Fine-tuned with the `speech_to_text_finetune.py` script from the NeMo Speech repository, warm-started from the pretrained base model (the base tokenizer was kept, i.e. no tokenizer update).
| Setting | Value |
|---|---|
| Base model | `nvidia/parakeet-tdt-0.6b-v3` |
| Dataset | AN4 (train: 948 utts, test: 130 utts) |
| Tokenizer | Reused from base model (no change) |
| Optimizer | AdamW |
| Learning rate | 1e-4 |
| LR schedule | CosineAnnealing, 100 warmup steps |
| Batch size | 16 |
| Epochs | 20 (1200 steps) |
| Precision | bf16 |
| Loss | TDT (Token-and-Duration Transducer) |
| Sample rate | 16 kHz mono |
Best validation WER during training reached ~0.041 (4.1%).
Evaluation
Evaluated on the AN4 test split (130 utterances), Word Error Rate (WER, lower is better):
| Model | Test WER |
|---|---|
| Baseline (`nvidia/parakeet-tdt-0.6b-v3`) | 75.81% |
| Fine-tuned (this model) | 0.39% |
How to read these numbers (important). The large gap is partly real adaptation and partly a scoring artifact. The baseline model outputs punctuation and capitalization and expects natural sentences, so on AN4's bare lowercase spelled-out labels it is penalized heavily by WER even when it recognizes the speech correctly. The fine-tuned model scores far lower mostly because it learned to match AN4's exact output format. A fairer comparison would normalize both hypotheses and references (lowercase, strip punctuation) before scoring, which would raise the baseline substantially and shrink the true gap. The fine-tune does genuinely help, but the headline delta overstates it.
License and attribution
This model is released under CC-BY-4.0, inherited from the base model.
It is a derivative of `nvidia/parakeet-tdt-0.6b-v3` by NVIDIA, which is licensed under CC-BY-4.0. Attribution to NVIDIA is required under the terms of that license. The AN4 dataset is used for training/evaluation.
Citation
If you use this model, please also credit the base model and framework:
- Base model: NVIDIA Parakeet TDT 0.6B v3 — https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3
- Framework: NVIDIA NeMo — https://github.com/NVIDIA-NeMo/Speech