Automatic Speech Recognition
NeMo
Safetensors
Transformers
PyTorch
English
parakeet_ctc
speech
audio
FastConformer
Conformer
NeMo
hf-asr-leaderboard
ctc
Eval Results (legacy)
Eval Results
Instructions to use nvidia/parakeet-ctc-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/parakeet-ctc-0.6b with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("nvidia/parakeet-ctc-0.6b") transcriptions = asr_model.transcribe(["file.wav"]) - Transformers
How to use nvidia/parakeet-ctc-0.6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nvidia/parakeet-ctc-0.6b")# Load model directly from transformers import AutoModelForCTC model = AutoModelForCTC.from_pretrained("nvidia/parakeet-ctc-0.6b", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ParakeetForCTC" | |
| ], | |
| "ctc_loss_reduction": "mean", | |
| "ctc_zero_infinity": true, | |
| "dtype": "bfloat16", | |
| "encoder_config": { | |
| "activation_dropout": 0.1, | |
| "attention_bias": true, | |
| "attention_dropout": 0.1, | |
| "conv_kernel_size": 9, | |
| "dropout": 0.1, | |
| "dropout_positions": 0.0, | |
| "hidden_act": "silu", | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layerdrop": 0.1, | |
| "max_position_embeddings": 5000, | |
| "model_type": "parakeet_encoder", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 8, | |
| "num_mel_bins": 80, | |
| "scale_input": true, | |
| "subsampling_conv_channels": 256, | |
| "subsampling_conv_kernel_size": 3, | |
| "subsampling_conv_stride": 2, | |
| "subsampling_factor": 8 | |
| }, | |
| "initializer_range": 0.02, | |
| "model_type": "parakeet_ctc", | |
| "pad_token_id": 1024, | |
| "transformers_version": "4.57.0.dev0", | |
| "vocab_size": 1025 | |
| } | |