Instructions to use speechbrain/asr-crdnn-switchboard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- speechbrain
How to use speechbrain/asr-crdnn-switchboard with speechbrain:
from speechbrain.pretrained import EncoderDecoderASR model = EncoderDecoderASR.from_hparams( "speechbrain/asr-crdnn-switchboard" ) model.transcribe_file("file.wav") - Notebooks
- Google Colab
- Kaggle
Download hyperparams.yaml from speechbrain/asr-crdnn-switchboard: direct link, hf CLI and curl.
- Browser
- Download file 4.52 kB
-
https://huggingface.co/speechbrain/asr-crdnn-switchboard/resolve/main/hyperparams.yaml
- Command line
-
hf download hf://speechbrain/asr-crdnn-switchboard/hyperparams.yaml
-
curl -L -o hyperparams.yaml https://huggingface.co/speechbrain/asr-crdnn-switchboard/resolve/main/hyperparams.yaml
4.52 kB
| # ############################################################################ | |
| # Model: E2E ASR with attention-based ASR | |
| # Encoder: CRDNN model | |
| # Decoder: GRU + beamsearch | |
| # Tokens: BPE with unigram | |
| # Losses: CTC + NLL | |
| # Training: Switchboard | |
| # Authors: Ju-Chieh Chou, Mirco Ravanelli, Abdel Heba, Peter Plantinga, | |
| # Samuele Cornell 2020, Dominik Wagner 2022 | |
| # ############################################################################ | |
| # Feature parameters | |
| sample_rate: 16000 | |
| n_fft: 400 | |
| n_mels: 40 | |
| # Model parameters | |
| activation: !name:torch.nn.LeakyReLU | |
| dropout: 0.15 | |
| cnn_blocks: 2 | |
| cnn_channels: (128, 256) | |
| inter_layer_pooling_size: (2, 2) | |
| cnn_kernelsize: (3, 3) | |
| time_pooling_size: 4 | |
| rnn_class: !name:speechbrain.nnet.RNN.LSTM | |
| rnn_layers: 4 | |
| rnn_neurons: 1024 | |
| rnn_bidirectional: True | |
| dnn_blocks: 2 | |
| dnn_neurons: 512 | |
| emb_size: 128 | |
| dec_neurons: 1024 | |
| output_neurons: 2000 # Number of tokens used for tokenizer | |
| blank_index: 0 | |
| bos_index: 1 | |
| eos_index: 2 | |
| # Decoding parameters | |
| min_decode_ratio: 0.0 | |
| max_decode_ratio: 1.0 | |
| valid_beam_size: 60 | |
| test_beam_size: 80 | |
| eos_threshold: 1.5 | |
| using_max_attn_shift: True | |
| max_attn_shift: 240 | |
| ctc_weight_decode: 0.3 | |
| coverage_penalty: 1.8 | |
| temperature: 1.25 | |
| normalizer: !new:speechbrain.processing.features.InputNormalization | |
| norm_type: global | |
| compute_features: !new:speechbrain.lobes.features.Fbank | |
| sample_rate: !ref <sample_rate> | |
| n_fft: !ref <n_fft> | |
| n_mels: !ref <n_mels> | |
| enc: !new:speechbrain.lobes.models.CRDNN.CRDNN | |
| input_shape: [null, null, !ref <n_mels>] | |
| activation: !ref <activation> | |
| dropout: !ref <dropout> | |
| cnn_blocks: !ref <cnn_blocks> | |
| cnn_channels: !ref <cnn_channels> | |
| cnn_kernelsize: !ref <cnn_kernelsize> | |
| inter_layer_pooling_size: !ref <inter_layer_pooling_size> | |
| time_pooling: True | |
| using_2d_pooling: False | |
| time_pooling_size: !ref <time_pooling_size> | |
| rnn_class: !ref <rnn_class> | |
| rnn_layers: !ref <rnn_layers> | |
| rnn_neurons: !ref <rnn_neurons> | |
| rnn_bidirectional: !ref <rnn_bidirectional> | |
| rnn_re_init: True | |
| dnn_blocks: !ref <dnn_blocks> | |
| dnn_neurons: !ref <dnn_neurons> | |
| use_rnnp: False | |
| emb: !new:speechbrain.nnet.embedding.Embedding | |
| num_embeddings: !ref <output_neurons> | |
| embedding_dim: !ref <emb_size> | |
| dec: !new:speechbrain.nnet.RNN.AttentionalRNNDecoder | |
| enc_dim: !ref <dnn_neurons> | |
| input_size: !ref <emb_size> | |
| rnn_type: gru | |
| attn_type: location | |
| hidden_size: !ref <dec_neurons> | |
| attn_dim: 1024 | |
| num_layers: 1 | |
| scaling: 1.0 | |
| channels: 10 | |
| kernel_size: 100 | |
| re_init: True | |
| dropout: !ref <dropout> | |
| ctc_lin: !new:speechbrain.nnet.linear.Linear | |
| input_size: !ref <dnn_neurons> | |
| n_neurons: !ref <output_neurons> | |
| seq_lin: !new:speechbrain.nnet.linear.Linear | |
| input_size: !ref <dec_neurons> | |
| n_neurons: !ref <output_neurons> | |
| log_softmax: !new:speechbrain.nnet.activations.Softmax | |
| apply_log: True | |
| tokenizer: !new:sentencepiece.SentencePieceProcessor | |
| asr_model: !new:torch.nn.ModuleList | |
| - [!ref <enc>, !ref <emb>, !ref <dec>, !ref <ctc_lin>, !ref <seq_lin>] | |
| encoder: !new:speechbrain.nnet.containers.LengthsCapableSequential | |
| input_shape: [null, null, !ref <n_mels>] | |
| compute_features: !ref <compute_features> | |
| normalize: !ref <normalizer> | |
| model: !ref <enc> | |
| ctc_scorer: !new:speechbrain.decoders.scorer.CTCScorer | |
| eos_index: !ref <eos_index> | |
| blank_index: !ref <blank_index> | |
| ctc_fc: !ref <ctc_lin> | |
| coverage_scorer: !new:speechbrain.decoders.scorer.CoverageScorer | |
| vocab_size: !ref <output_neurons> | |
| scorer: !new:speechbrain.decoders.scorer.ScorerBuilder | |
| full_scorers: [!ref <ctc_scorer>, !ref <coverage_scorer>] | |
| weights: | |
| ctc: !ref <ctc_weight_decode> | |
| coverage: !ref <coverage_penalty> | |
| decoder: !new:speechbrain.decoders.S2SRNNBeamSearcher | |
| embedding: !ref <emb> | |
| decoder: !ref <dec> | |
| linear: !ref <seq_lin> | |
| bos_index: !ref <bos_index> | |
| eos_index: !ref <eos_index> | |
| min_decode_ratio: !ref <min_decode_ratio> | |
| max_decode_ratio: !ref <max_decode_ratio> | |
| beam_size: !ref <test_beam_size> | |
| eos_threshold: !ref <eos_threshold> | |
| using_max_attn_shift: !ref <using_max_attn_shift> | |
| max_attn_shift: !ref <max_attn_shift> | |
| temperature: !ref <temperature> | |
| scorer: !ref <scorer> | |
| modules: | |
| normalizer: !ref <normalizer> | |
| encoder: !ref <encoder> | |
| decoder: !ref <decoder> | |
| ctc_lin: !ref <ctc_lin> | |
| pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer | |
| loadables: | |
| normalizer: !ref <normalizer> | |
| asr: !ref <asr_model> | |
| tokenizer: !ref <tokenizer> | |