metadata
language:
- pl
license: cc-by-4.0
library_name: nemo
tags:
- automatic-speech-recognition
- speech
- nemo
- parakeet
- fastconformer
- tdt
- polish
- nvidia
- common-voice
- bigos
- fine-tuned
datasets:
- amu-cai/pl-asr-bigos-v2
- fixie-ai/common_voice_17_0
base_model: nvidia/parakeet-tdt-0.6b-v3
pipeline_tag: automatic-speech-recognition
model-index:
- name: parakeet-tdt-0.6b-polish
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: Common Voice 17.0 (pl) - Validation
type: fixie-ai/common_voice_17_0
config: pl
split: validation
metrics:
- type: wer
value: 6.07
name: Val WER
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: Common Voice 17.0 (pl) - Test
type: fixie-ai/common_voice_17_0
config: pl
split: test
metrics:
- type: wer
value: 11.81
name: Test WER
- type: cer
value: 2.72
name: Test CER
Parakeet-TDT-0.6B Polish
A Polish automatic speech recognition (ASR) model fine-tuned from nvidia/parakeet-tdt-0.6b-v3.
Model Details
| Property | Value |
|---|---|
| Base model | nvidia/parakeet-tdt-0.6b-v3 |
| Architecture | FastConformer-TDT (600M params) |
| Language | Polish (pl) |
| Input | 16 kHz mono audio |
| Output | Polish text with punctuation and capitalization |
| License | CC-BY-4.0 |
Evaluation Results
Evaluated on Common Voice 17.0 Polish (raw text, no normalization):
| Split | WER | CER | Samples |
|---|---|---|---|
| Validation | 6.07% | -- | -- |
| Test | 11.81% | 2.72% | 9,230 |
Training
Fine-tuned on a curated subset of the BIGOS v2 benchmark, filtered to retain only sources with proper casing and punctuation:
- Common Voice 15 -- 19,119 human-recorded Polish speech samples
- M-AILABS / LibriVox -- 11,834 read Polish audiobook samples
- PolyAI Minds14 -- 462 Polish banking dialog samples
- Total training set: ~31,415 samples
Validation uses the BIGOS v2 validation split (same source filtering). Test evaluation uses Common Voice 17.0 Polish (independent test set).
Training Configuration
| Parameter | Value |
|---|---|
| Optimizer | AdamW |
| Learning rate | 5e-5 (cosine annealing) |
| Warmup | 10% of total steps |
| Batch size | 32 |
| Precision | bf16-mixed |
| Gradient clipping | 1.0 |
| Early stopping | 10 epochs patience on val WER |
| Best epoch | 21 |
Usage
Installation
pip install nemo_toolkit[asr]
Transcribe Audio
import nemo.collections.asr as nemo_asr
# Load model
asr_model = nemo_asr.models.ASRModel.from_pretrained(
model_name="yuriyvnv/parakeet-tdt-0.6b-polish"
)
# Transcribe
output = asr_model.transcribe(["audio.wav"])
print(output[0].text)
Transcribe with Timestamps
output = asr_model.transcribe(["audio.wav"], timestamps=True)
for stamp in output[0].timestamp["segment"]:
print(f"{stamp['start']:.1f}s - {stamp['end']:.1f}s : {stamp['segment']}")
Long-Form Audio
For audio longer than 24 minutes, enable local attention:
asr_model.change_attention_model(
self_attention_model="rel_pos_local_attn",
att_context_size=[256, 256],
)
output = asr_model.transcribe(["long_audio.wav"])
Intended Use
This model is designed for transcribing Polish speech to text. It works best on:
- Read speech and conversational Polish
- Audio recorded at 16 kHz or higher
- Segments up to 24 minutes (or longer with local attention enabled)
Limitations
- Training data is sourced from read speech (audiobooks, Common Voice read prompts) and short banking dialogs; performance may differ on spontaneous or heavily accented speech
- The model preserves punctuation and capitalization as seen in training data
- Not suitable for real-time streaming without additional configuration