Pruned Stateless Zipformer RNN-T Streaming Robust SW v4

Pruned Stateless Zipformer RNN-T Streaming Robust SW v4 is an automatic speech recognition model trained on the following datasets:

Instead of being trained to predict sequences of words, this model was trained to predict sequence of phonemes, e.g. ["w", "ɑ", "ʃ", "i", "ɑ"]. Therefore, the model's vocabulary contains the different IPA phonemes found in gruut.

This model was trained using icefall framework. All training was done on 2 NVIDIA RTX 4090 GPUs. All necessary scripts used for training could be found in the Files and versions tab, as well as the Training metrics logged via Tensorboard.

Evaluation Results

Simulated Streaming

for m in greedy_search fast_beam_search modified_beam_search; do
  ./zipformer/decode.py \
    --epoch 40 \
    --avg 7 \
    --causal 1 \
    --num-encoder-layers 2,2,2,2,2,2 \
    --feedforward-dim 512,768,768,768,768,768 \
    --encoder-dim 192,256,256,256,256,256 \
    --encoder-unmasked-dim 192,192,192,192,192,192 \
    --chunk-size 32 \
    --left-context-frames 128 \
    --exp-dir zipformer/exp-causal \
    --use-transducer True --use-ctc True \
    --decoding-method $m
done

./zipformer/ctc_decode.py \
    --epoch 40 \
    --avg 7 \
    --causal 1 \
    --num-encoder-layers 2,2,2,2,2,2 \
    --feedforward-dim 512,768,768,768,768,768 \
    --encoder-dim 192,256,256,256,256,256 \
    --encoder-unmasked-dim 192,192,192,192,192,192 \
    --chunk-size 32 \
    --left-context-frames 128 \
    --exp-dir zipformer/exp-causal \
    --decoding-method ctc-decoding \
    --use-transducer True --use-ctc True

The model achieves the following phoneme error rates on the different test sets:

Decoding	Common Voice 16.1	FLEURS	Bookbot
Greedy Search	7.64%	6.74%	1.98%
Modified Beam Search	7.77%	6.69%	2.26%
Fast Beam Search	8.18%	6.83%	2.84%
CTC Greedy Search	7.65%	6.66%	4.26%

Chunk-wise Streaming

for m in greedy_search fast_beam_search modified_beam_search; do
  ./zipformer/streaming_decode.py \
    --epoch 40 \
    --avg 7 \
    --causal 1 \
    --num-encoder-layers 2,2,2,2,2,2 \
    --feedforward-dim 512,768,768,768,768,768 \
    --encoder-dim 192,256,256,256,256,256 \
    --encoder-unmasked-dim 192,192,192,192,192,192 \
    --chunk-size 32 \
    --left-context-frames 128 \
    --exp-dir zipformer/exp-causal \
    --use-transducer True --use-ctc True \
    --decoding-method $m \
    --num-decode-streams 1000
done

The model achieves the following phoneme error rates on the different test sets:

Decoding	Common Voice 16.1	FLEURS	Bookbot
Greedy Search	7.68%	6.71%	2.12%
Modified Beam Search	7.52%	6.64%	2.07%
Fast Beam Search	8.2%	6.76%	2.88%

Usage

Download Pre-trained Model

cd egs/bookbot_sw/ASR
mkdir tmp
cd tmp
git lfs install
git clone https://huggingface.co/bookbot/zipformer-streaming-robust-sw-v4/

Inference

To decode with greedy search, run:

./zipformer/jit_pretrained_streaming.py \
  --nn-model-filename ./tmp/zipformer-streaming-robust-sw-v4/exp-causal/jit_script_chunk_32_left_128.pt \
  --tokens ./tmp/zipformer-streaming-robust-sw-v4/data/lang_phone/tokens.txt \
  ./tmp/zipformer-streaming-robust-sw-v4/test_waves/sample1.wav

Decoding Output

2024-10-28 13:54:44,964 INFO [jit_pretrained_streaming.py:184] device: cuda:0
2024-10-28 13:54:45,325 INFO [jit_pretrained_streaming.py:197] Constructing Fbank computer
2024-10-28 13:54:45,325 INFO [jit_pretrained_streaming.py:200] Reading sound files: ./tmp/zipformer-streaming-robust-sw-v4/test_waves/sample1.wav
2024-10-28 13:54:45,353 INFO [jit_pretrained_streaming.py:205] torch.Size([125568])
2024-10-28 13:54:45,353 INFO [jit_pretrained_streaming.py:207] Decoding started
2024-10-28 13:54:45,353 INFO [jit_pretrained_streaming.py:212] chunk_length: 64
2024-10-28 13:54:45,353 INFO [jit_pretrained_streaming.py:213] T: 77
2024-10-28 13:54:45,364 INFO [jit_pretrained_streaming.py:229] 0/130368
2024-10-28 13:54:45,366 INFO [jit_pretrained_streaming.py:229] 4000/130368
2024-10-28 13:54:45,367 INFO [jit_pretrained_streaming.py:229] 8000/130368
2024-10-28 13:54:45,367 INFO [jit_pretrained_streaming.py:229] 12000/130368
2024-10-28 13:54:45,535 INFO [jit_pretrained_streaming.py:229] 16000/130368
2024-10-28 13:54:45,536 INFO [jit_pretrained_streaming.py:229] 20000/130368
2024-10-28 13:54:45,545 INFO [jit_pretrained_streaming.py:229] 24000/130368
2024-10-28 13:54:45,546 INFO [jit_pretrained_streaming.py:229] 28000/130368
2024-10-28 13:54:45,547 INFO [jit_pretrained_streaming.py:229] 32000/130368
2024-10-28 13:54:45,556 INFO [jit_pretrained_streaming.py:229] 36000/130368
2024-10-28 13:54:45,557 INFO [jit_pretrained_streaming.py:229] 40000/130368
2024-10-28 13:54:45,566 INFO [jit_pretrained_streaming.py:229] 44000/130368
2024-10-28 13:54:45,567 INFO [jit_pretrained_streaming.py:229] 48000/130368
2024-10-28 13:54:45,567 INFO [jit_pretrained_streaming.py:229] 52000/130368
2024-10-28 13:54:45,576 INFO [jit_pretrained_streaming.py:229] 56000/130368
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2024-10-28 13:54:45,597 INFO [jit_pretrained_streaming.py:229] 76000/130368
2024-10-28 13:54:45,598 INFO [jit_pretrained_streaming.py:229] 80000/130368
2024-10-28 13:54:45,599 INFO [jit_pretrained_streaming.py:229] 84000/130368
2024-10-28 13:54:45,608 INFO [jit_pretrained_streaming.py:229] 88000/130368
2024-10-28 13:54:45,609 INFO [jit_pretrained_streaming.py:229] 92000/130368
2024-10-28 13:54:45,618 INFO [jit_pretrained_streaming.py:229] 96000/130368
2024-10-28 13:54:45,619 INFO [jit_pretrained_streaming.py:229] 100000/130368
2024-10-28 13:54:45,619 INFO [jit_pretrained_streaming.py:229] 104000/130368
2024-10-28 13:54:45,628 INFO [jit_pretrained_streaming.py:229] 108000/130368
2024-10-28 13:54:45,629 INFO [jit_pretrained_streaming.py:229] 112000/130368
2024-10-28 13:54:45,638 INFO [jit_pretrained_streaming.py:229] 116000/130368
2024-10-28 13:54:45,639 INFO [jit_pretrained_streaming.py:229] 120000/130368
2024-10-28 13:54:45,640 INFO [jit_pretrained_streaming.py:229] 124000/130368
2024-10-28 13:54:45,649 INFO [jit_pretrained_streaming.py:229] 128000/130368
2024-10-28 13:54:45,649 INFO [jit_pretrained_streaming.py:259] ./tmp/zipformer-streaming-robust-sw-v4/test_waves/sample1.wav
2024-10-28 13:54:45,649 INFO [jit_pretrained_streaming.py:260] wɑʃiɑɑᵐɓɑɔwɑnɑiʃihɑsɑkɑtikɑɛnɛɔlɑmɑʃɑɾikikɑtikɑufɑlmɛhuɔwɛnjɛutɑʄiɾiwɑmɑfutɑ
2024-10-28 13:54:45,649 INFO [jit_pretrained_streaming.py:262] Decoding Done

Training procedure

Install icefall

git clone https://github.com/bookbot-hive/icefall
cd icefall
export PYTHONPATH=`pwd`:$PYTHONPATH

Prepare Data

cd egs/bookbot_sw/ASR
./prepare.sh

Train

export CUDA_VISIBLE_DEVICES="0,1"
./zipformer/train.py \
  --world-size 2 \
  --num-epochs 40 \
  --use-fp16 1 \
  --exp-dir zipformer/exp-causal \
  --causal 1 \
  --num-encoder-layers 2,2,2,2,2,2 \
  --feedforward-dim 512,768,768,768,768,768 \
  --encoder-dim 192,256,256,256,256,256 \
  --encoder-unmasked-dim 192,192,192,192,192,192 \
  --base-lr 0.04 \
  --max-duration 400 \
  --use-transducer True --use-ctc True

Frameworks

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Swahili ASR

Collection

Swahili ASR models and datasets. • 16 items • Updated 10 days ago