Instructions to use asafaya/hubert-large-arabic-transcribe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asafaya/hubert-large-arabic-transcribe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="asafaya/hubert-large-arabic-transcribe")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("asafaya/hubert-large-arabic-transcribe") model = AutoModel.from_pretrained("asafaya/hubert-large-arabic-transcribe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| sample_rate: 16000 | |
| # BPE parameters | |
| token_type: unigram # ["unigram", "bpe", "char"] | |
| character_coverage: 1.0 | |
| # Model parameters | |
| activation: !name:torch.nn.GELU | |
| wav2vec_output_dim: 1024 | |
| dnn_neurons: 1024 | |
| freeze_wav2vec: false | |
| dropout: 0.2 | |
| # Outputs | |
| output_neurons: 80 # BPE size, index(blank/eos/bos) = 0 | |
| tokenizer: !new:sentencepiece.SentencePieceProcessor | |
| # Decoding parameters | |
| # Be sure that the bos and eos index match with the BPEs ones | |
| blank_index: 0 | |
| bos_index: 1 | |
| eos_index: 2 | |
| enc: &id002 !new:speechbrain.nnet.containers.Sequential | |
| input_shape: [null, null, 1024] | |
| linear1: !name:speechbrain.nnet.linear.Linear | |
| n_neurons: 1024 | |
| bias: true | |
| bn1: !name:speechbrain.nnet.normalization.BatchNorm1d | |
| activation: !new:torch.nn.GELU | |
| drop: !new:torch.nn.Dropout | |
| p: 0.2 | |
| linear2: !name:speechbrain.nnet.linear.Linear | |
| n_neurons: 1024 | |
| bias: true | |
| bn2: !name:speechbrain.nnet.normalization.BatchNorm1d | |
| activation2: !new:torch.nn.GELU | |
| drop2: !new:torch.nn.Dropout | |
| p: 0.2 | |
| linear3: !name:speechbrain.nnet.linear.Linear | |
| n_neurons: 1024 | |
| bias: true | |
| bn3: !name:speechbrain.nnet.normalization.BatchNorm1d | |
| activation3: !new:torch.nn.GELU | |
| wav2vec2: !new:speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWav2Vec2 | |
| source: asafaya/hubert-large-arabic-transcribe | |
| output_norm: true | |
| freeze: false | |
| save_path: wav2vec2_checkpoint | |
| ctc_lin: !new:speechbrain.nnet.linear.Linear | |
| input_size: 1024 | |
| n_neurons: 80 | |
| log_softmax: !new:speechbrain.nnet.activations.Softmax | |
| apply_log: true | |
| ctc_cost: !name:speechbrain.nnet.losses.ctc_loss | |
| blank_index: 0 | |
| modules: | |
| encoder: !new:speechbrain.nnet.containers.LengthsCapableSequential | |
| wav2vec2: !ref <wav2vec2> | |
| enc: !ref <enc> | |
| ctc_lin: !ref <ctc_lin> | |
| model: !new:torch.nn.ModuleList | |
| - [!ref <enc>, !ref <ctc_lin>] | |
| error_rate_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats | |
| cer_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats | |
| split_tokens: true | |
| decoding_function: !name:speechbrain.decoders.ctc.ctc_greedy_decode | |
| blank_id: !ref <blank_index> | |
| pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer | |
| loadables: | |
| wav2vec2: !ref <wav2vec2> | |
| model: !ref <model> | |
| tokenizer: !ref <tokenizer> | |