Pretrained wieghts
Generated Librispeech KenLM scorer
cd venv/bin
./pip install -r ../../requirements.txt
./pip install deepspeed==0.3.13SpeechRecognitionModel(
# First convolutional layer
(cnn): Conv2d(1, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
# 7 blocks of convolutional layers with residual connections
(res_cnn): Sequential(
(0): ResidualCNN(
(norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(gelu): GELU()
(dropout): Dropout(p=0.2, inplace=False)
(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): ResidualCNN(
(norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(gelu): GELU()
(dropout): Dropout(p=0.2, inplace=False)
(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): ResidualCNN(
(norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(gelu): GELU()
(dropout): Dropout(p=0.2, inplace=False)
(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): ResidualCNN(
(norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(gelu): GELU()
(dropout): Dropout(p=0.2, inplace=False)
(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): ResidualCNN(
(norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(gelu): GELU()
(dropout): Dropout(p=0.2, inplace=False)
(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(5): ResidualCNN(
(norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(gelu): GELU()
(dropout): Dropout(p=0.2, inplace=False)
(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(6): ResidualCNN(
(norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(gelu): GELU()
(dropout): Dropout(p=0.2, inplace=False)
(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
# Single linear layer to tie CNN and RNN parts together
(fc): Linear(in_features=2048, out_features=512, bias=True)
# 5 blocks of recurrent layers
(rnn): Sequential(
(0): RNN(
(norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(gelu): GELU()
(gru): GRU(512, 512, batch_first=True, bidirectional=True)
(dropout): Dropout(p=0.2, inplace=False)
)
(1): RNN(
(norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(gelu): GELU()
(gru): GRU(1024, 512, bidirectional=True)
(dropout): Dropout(p=0.2, inplace=False)
)
(2): RNN(
(norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(gelu): GELU()
(gru): GRU(1024, 512, bidirectional=True)
(dropout): Dropout(p=0.2, inplace=False)
)
(3): RNN(
(norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(gelu): GELU()
(gru): GRU(1024, 512, bidirectional=True)
(dropout): Dropout(p=0.2, inplace=False)
)
(4): RNN(
(norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(gelu): GELU()
(gru): GRU(1024, 512, bidirectional=True)
(dropout): Dropout(p=0.2, inplace=False)
)
)
# Two fully connected layers to convert the output of RNN layers to a probability
# distribution for each vertical feature vector and each character
(dense): Sequential(
(0): Linear(in_features=1024, out_features=512, bias=True)
(1): GELU()
(2): Dropout(p=0.2, inplace=False)
(3): Linear(in_features=512, out_features=29, bias=True)
)
)# Download LibriSpeech texts
wget http://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz
# Download and unpack DeepSpeech native client
wget https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/native_client.amd64.cpu.osx.tar.xz
# Download and save this Python script
# https://github.com/mozilla/DeepSpeech/blob/master/data/lm/generate_lm.py
# Generate lm.binary and vocab-500000.txt
python3 generate_lm.py --input_txt librispeech-lm-norm.txt.gz --output_dir . \
--top_k 500000 --kenlm_bins ./ \
--arpa_order 5 --max_arpa_memory "85%" --arpa_prune "0|0|1" \
--binary_a_bits 255 --binary_q_bits 8 --binary_type trie
# Generate kenlm.scorer using lm.binary, chars.txt (generated by our model), and vocab-500000.txt
# Please note that generate_scorer_package is a part of DeepSpeech native client
./generate_scorer_package --alphabet ../../chars.txt --lm ../../lm.binary --vocab ../../vocab-500000.txt \
--package kenlm.scorer --default_alpha 0.931289039105002 --default_beta 1.1834137581510284
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