Automatic Speech Recognition
NeMo
PyTorch
English
speech
audio
Transducer
TDT
FastConformer
Conformer
NeMo
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use nvidia/parakeet-tdt-0.6b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/parakeet-tdt-0.6b-v2 with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("nvidia/parakeet-tdt-0.6b-v2") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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This model was trained using the NeMo toolkit [3], following the strategies below:
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- Initialized from a
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- Trained for 150,000 steps on
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- Dataset corpora were balanced using a temperature sampling value of 0.5.
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- Stage 2 fine-tuning was performed for 2,500 steps on 4 A100 GPUs using approximately 500 hours of high-quality, human-transcribed data of NeMo ASR Set 3.0.
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This model was trained using the NeMo toolkit [3], following the strategies below:
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- Initialized from a FastConformer SSL checkpoint that was pretrained with a wav2vev method on the LibriLight dataset[7].
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- Trained for 150,000 steps on 64 A100 GPUs.
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- Dataset corpora were balanced using a temperature sampling value of 0.5.
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- Stage 2 fine-tuning was performed for 2,500 steps on 4 A100 GPUs using approximately 500 hours of high-quality, human-transcribed data of NeMo ASR Set 3.0.
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