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ASR Microservice

ASR (Audio-Speech-Recognition) microservice helps users convert speech to text. When building a talking bot with LLM, users will need to convert their audio inputs (What they talk, or Input audio from other sources) to text, so the LLM is able to tokenize the text and generate an answer. This microservice is built for that conversion stage.

🚀1. Start Microservice with Python (Option 1)

To start the ASR microservice with Python, you need to first install python packages.

1.1 Install Requirements

pip install -r requirements.txt

1.2 Start Whisper Service/Test

  • Xeon CPU
cd dependency/
nohup python whisper_server.py --device=cpu &
python check_whisper_server.py

Note: please make sure that port 7066 is not occupied by other services. Otherwise, use the command npx kill-port 7066 to free the port.

If the Whisper server is running properly, you should see the following output:

{'asr_result': 'Who is pat gelsinger'}
  • Gaudi2 HPU
pip install optimum[habana]

cd dependency/
nohup python whisper_server.py --device=hpu &
python check_whisper_server.py

# Or use openai protocol compatible curl command
# Please refer to https://platform.openai.com/docs/api-reference/audio/createTranscription
wget https://github.com/intel/intel-extension-for-transformers/raw/main/intel_extension_for_transformers/neural_chat/assets/audio/sample.wav
curl http://localhost:7066/v1/audio/transcriptions \
  -H "Content-Type: multipart/form-data" \
  -F file="@./sample.wav" \
  -F model="openai/whisper-small"

1.3 Start ASR Service/Test

cd ../
python asr.py
python check_asr_server.py

While the Whisper service is running, you can start the ASR service. If the ASR service is running properly, you should see the output similar to the following:

{'id': '0e686efd33175ce0ebcf7e0ed7431673', 'text': 'who is pat gelsinger'}

🚀2. Start Microservice with Docker (Option 2)

Alternatively, you can also start the ASR microservice with Docker.

2.1 Build Images

2.1.1 Whisper Server Image

  • Xeon CPU
cd ../..
docker build -t opea/whisper:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/asr/whisper/dependency/Dockerfile .
  • Gaudi2 HPU
cd ../..
docker build -t opea/whisper-gaudi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/asr/whisper/dependency/Dockerfile.intel_hpu .

2.1.2 ASR Service Image

docker build -t opea/asr:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/asr/whisper/Dockerfile .

2.2 Start Whisper and ASR Service

2.2.1 Start Whisper Server

  • Xeon
docker run -p 7066:7066 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/whisper:latest
  • Gaudi2 HPU
docker run -p 7066:7066 --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/whisper-gaudi:latest

2.2.2 Start ASR service

ip_address=$(hostname -I | awk '{print $1}')

docker run -d -p 9099:9099 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e ASR_ENDPOINT=http://$ip_address:7066 opea/asr:latest

2.2.3 Test

# Use curl or python

# curl
http_proxy="" curl http://localhost:9099/v1/audio/transcriptions -XPOST -d '{"byte_str": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}' -H 'Content-Type: application/json'


# python
python check_asr_server.py