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
Download tokenizer_config.json from CollectionStudio/parakeet-rnnt-0.6b: direct link, hf CLI and curl.
- Browser
- Download file 260 Bytes
-
https://huggingface.co/CollectionStudio/parakeet-rnnt-0.6b/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://CollectionStudio/parakeet-rnnt-0.6b/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/CollectionStudio/parakeet-rnnt-0.6b/resolve/main/tokenizer_config.json
260 Bytes
| { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": false, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<blank>", | |
| "processor_class": "ParakeetProcessor", | |
| "tokenizer_class": "ParakeetTokenizer", | |
| "unk_token": "<unk>" | |
| } | |