Automatic Speech Recognition
NeMo
Safetensors
PyTorch
Transformers
English
parakeet_rnnt
feature-extraction
speech
audio
Transducer
FastConformer
Conformer
NeMo
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use CollectionStudio/parakeet-rnnt-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use CollectionStudio/parakeet-rnnt-0.6b with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("CollectionStudio/parakeet-rnnt-0.6b") transcriptions = asr_model.transcribe(["file.wav"]) - Transformers
How to use CollectionStudio/parakeet-rnnt-0.6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CollectionStudio/parakeet-rnnt-0.6b")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CollectionStudio/parakeet-rnnt-0.6b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 260 Bytes
adb61da | 1 2 3 4 5 6 7 8 9 10 | {
"backend": "tokenizers",
"clean_up_tokenization_spaces": false,
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<blank>",
"processor_class": "ParakeetProcessor",
"tokenizer_class": "ParakeetTokenizer",
"unk_token": "<unk>"
}
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