k2-sherpa 0.6

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Documentation: https://k2-fsa.github.io/sherpa/

Introduction

An ASR server framework in Python, supporting both streaming and non-streaming recognition.

CPU-bound tasks, such as neural network computation, are implemented in C++; while IO-bound tasks, such as socket communication, are implemented in Python.

Caution: For offline ASR, we assume the model is trained using pruned stateless RNN-T from icefall and it is from a directory like pruned_transducer_statelessX where X >=2. For streaming ASR, we assume the model is using pruned_stateless_emformer_rnnt2.

For the offline ASR, we provide a Colab notebook, containing how to start the server, how to start the client, and how to decode test-clean of LibriSpeech.

For the streaming ASR, we provide a YouTube demo, showing you how to use it. See https://www.youtube.com/watch?v=z7HgaZv5W0U

Installation

Please refer to https://k2-fsa.github.io/sherpa/installation/index.html for installation.

Usage

First, check that sherpa has been installed successfully:

python3 -c "import sherpa; print(sherpa.__version__)"

It should print the version of sherpa.

Visit https://k2-fsa.github.io/sherpa/ to see more tutorials of sherpa.

Streaming ASR with pruned stateless Emformer RNN-T

Start the server

To start the server, you need to first generate two files:

• (1) The torch script model file. You can use export.py --jit=1 in pruned_stateless_emformer_rnnt2 from icefall.

• (2) The BPE model file. You can find it in data/lang_bpe_XXX/bpe.model in icefall, where XXX is the number of BPE tokens used in the training.

With the above two files ready, you can start the server with the following command:

./sherpa/bin/pruned_stateless_emformer_rnnt2/streaming_server.py \
  --port 6006 \
  --max-batch-size 50 \
  --max-wait-ms 5 \
  --max-active-connections 500 \
  --nn-pool-size 1 \
  --nn-model-filename ./path/to/exp/cpu_jit.pt \
  --bpe-model-filename ./path/to/data/lang_bpe_500/bpe.model

You can use ./sherpa/bin/pruned_stateless_emformer_rnnt2/streaming_server.py --help to view the help message.

Hint: You can use the environment variable CUDA_VISIBLE_DEVICES to control which GPU is used. For instance, to use GPU 3 in the server, just set export CUDA_VISIBLE_DEVICES="3" before starting the server.

Note: To keep the server from OOM error, please tune --max-batch-size and --max-active-connections.

We provide a pretrained model using the LibriSpeech dataset at https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-stateless-emformer-rnnt2-2022-06-01

The following shows how to use the above pretrained model to start the server.

git lfs install
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-stateless-emformer-rnnt2-2022-06-01

./sherpa/bin/pruned_stateless_emformer_rnnt2/streaming_server.py \
  --port 6006 \
  --max-batch-size 50 \
  --max-wait-ms 5 \
  --nn-pool-size 1 \
  --nn-model-filename ./icefall-asr-librispeech-pruned-stateless-emformer-rnnt2-2022-06-01/exp/cpu_jit-epoch-39-avg-6-use-averaged-model-1.pt \
  --bpe-model-filename ./icefall-asr-librispeech-pruned-stateless-emformer-rnnt2-2022-06-01/data/lang_bpe_500/bpe.model

Here, before running the web client, you need to map your server ports to your local ports in the server terminal firstly with the following command:

ssh -R 6006:localhost:6006 -R 6008:localhost:6008 your_local_username@your_local_ip

Note: (1) You only need to do this if the asr server is running on a machine different from the client. (2) The command is run in the terminal on the server machine.

Start the client

We provide two clients at present:

• (1) ./sherpa/bin/pruned_stateless_emformer_rnnt2/streaming_client.py It shows how to decode a single sound file.

• (2) ./sherpa/bin/pruned_stateless_emformer_rnnt2/web You can record your speech in real-time within a browser and send it to the server for recognition.

streaming_client.py

./sherpa/bin/pruned_stateless_emformer_rnnt2/streaming_client.py --help

./sherpa/bin/pruned_stateless_emformer_rnnt2/streaming_client.py \
  --server-addr localhost \
  --server-port 6006 \
  ./icefall-asr-librispeech-pruned-stateless-emformer-rnnt2-2022-06-01/test_wavs/1221-135766-0001.wav

Web client

cd ./sherpa/bin/web
python3 -m http.server 6008

Then open your browser and go to http://localhost:6008/record.html. You will see a UI like the following screenshot.

Click the button Record.

Now you can speak and you will get recognition results from the server in real-time.

Caution: For the web client, we hard-code the server port to 6006. You can change the file ./sherpa/bin/web/record.js to replace 6006 in it to whatever port the server is using.

Caution: http://0.0.0.0:6008/record.html or http://127.0.0.1:6008/record.html won't work. You have to use localhost. Otherwise, you won't be able to use your microphone in your browser since we are not using https which requires a certificate.

Offline ASR

Start the server

To start the server, you need to first generate two files:

• (1) The torch script model file. You can use export.py --jit=1 in pruned_transducer_statelessX from icefall.

• (2) The BPE model file. You can find it in data/lang_bpe_XXX/bpe.model in icefall, where XXX is the number of BPE tokens used in the training. If you use a dataset like aishell to train your model where the modeling unit is Chinese characters, you need to provide a tokens.txt file which can be found in data/lang_char/tokens.txt in icefall.

With the above two files ready, you can start the server with the following command:

# If you provide a bpe.model, e.g., for LibriSpeech,
# you can use the following command:
#
sherpa/bin/conformer_rnnt/offline_server.py \
  --port 6006 \
  --num-device 1 \
  --max-batch-size 10 \
  --max-wait-ms 5 \
  --max-active-connections 500 \
  --feature-extractor-pool-size 5 \
  --nn-pool-size 1 \
  --nn-model-filename ./path/to/exp/cpu_jit.pt \
  --bpe-model-filename ./path/to/data/lang_bpe_500/bpe.model

# If you provide a tokens.txt, e.g., for aishell,
# you can use the following command:
#
sherpa/bin/conformer_rnnt/offline_server.py \
  --port 6006 \
  --num-device 1 \
  --max-batch-size 10 \
  --max-wait-ms 5 \
  --max-active-connections 500 \
  --feature-extractor-pool-size 5 \
  --nn-pool-size 1 \
  --nn-model-filename ./path/to/exp/cpu_jit.pt \
  --token-filename ./path/to/data/lang_char/tokens.txt

You can use ./sherpa/bin/conformer_rnnt/offline_server.py --help to view the help message.

HINT: If you don't have GPU, please set --num-device to 0.

Caution: To keep the server from out-of-memory error, you can tune --max-batch-size and --max-active-connections.

We provide pretrained models for the following two datasets:

• (1) LibriSpeech: https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13 It uses a BPE model with vocabulary size 500.

• (2) aishell: https://huggingface.co/csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20 It uses Chinese characters as models units. The vocabulary size is 4336.

The following shows how to use the above pretrained models to start the server.

• Use the pretrained model trained with the Librispeech dataset

git lfs install
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13

sherpa/bin/conformer_rnnt/offline_server.py \
  --port 6006 \
  --num-device 1 \
  --max-batch-size 10 \
  --max-wait-ms 5 \
  --max-active-connections 500 \
  --feature-extractor-pool-size 5 \
  --nn-pool-size 1 \
  --nn-model-filename ./icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/exp/cpu_jit.pt \
  --bpe-model-filename ./icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/data/lang_bpe_500/bpe.model

• For the pretrained model trained with the aishell dataset

git lfs install
git clone https://huggingface.co/csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20

sherpa/bin/conformer_rnnt/offline_server.py \
  --port 6006 \
  --num-device 1 \
  --max-batch-size 10 \
  --max-wait-ms 5 \
  --max-active-connections 500 \
  --feature-extractor-pool-size 5 \
  --nn-pool-size 1 \
  --nn-model-filename ./icefall-aishell-pruned-transducer-stateless3-2022-06-20/exp/cpu_jit-epoch-29-avg-5-torch-1.6.0.pt \
  --token-filename ./icefall-aishell-pruned-transducer-stateless3-2022-06-20/data/lang_char/tokens.txt

Start the client

After starting the server, you can use the following command to start the client:

./sherpa/bin/conformer_rnnt/offline_client.py \
    --server-addr localhost \
    --server-port 6006 \
    /path/to/foo.wav \
    /path/to/bar.wav

You can use ./sherpa/bin/conformer_rnnt/offline_client.py --help to view the usage message.

The following shows how to use the client to send some test waves to the server for recognition.

# If you use the pretrained model from the LibriSpeech dataset
sherpa/bin/conformer_rnnt/offline_client.py \
  --server-addr localhost \
  --server-port 6006 \
  icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13//test_wavs/1089-134686-0001.wav \
  icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13//test_wavs/1221-135766-0001.wav \
  icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13//test_wavs/1221-135766-0002.wav

# If you use the pretrained model from the aishell dataset
sherpa/bin/conformer_rnnt/offline_client.py \
  --server-addr localhost \
  --server-port 6006 \
  ./icefall-aishell-pruned-transducer-stateless3-2022-06-20/test_wavs/BAC009S0764W0121.wav \
  ./icefall-aishell-pruned-transducer-stateless3-2022-06-20/test_wavs/BAC009S0764W0122.wav \
  ./icefall-aishell-pruned-transducer-stateless3-2022-06-20/test_wavs/BAC009S0764W0123.wav

RTF test

We provide a demo ./sherpa/bin/conformer_rnnt/decode_manifest.py to decode the test-clean dataset from the LibriSpeech corpus.

It creates 50 connections to the server using websockets and sends audio files to the server for recognition.

At the end, it will display the RTF and the WER.

To give you an idea of the performance of the pretrained model, the Colab notebook shows the following results:

RTF: 0.0094
total_duration: 19452.481 seconds (5.40 hours)
processing time: 183.305 seconds (0.05 hours)
%WER = 2.06

Errors: 112 insertions, 93 deletions, 876 substitutions, over 52576 reference words (51607 correct)

If you have a GPU with a larger RAM (e.g., 32 GB), you can get an even lower RTF.