Whisper Streaming is a Ray Serve-based ASR solution that enables near-realtime audio streaming and transcription. The system employs Huggingface's Voice Activity Detection (VAD) and OpenAI's Whisper model (faster-whisper being the default) for accurate speech recognition and processing.
The real-time streaming ASR can be used in the following use cases:
- closed caption
- dictation, email, messaging
- dialogs systems
- front-end for online conference translation
- court protocols
- any sort of online transcription of microphone data
The project is based on VoiceStreamAI (https://github.com/alesaccoia/VoiceStreamAI). It is composed of containing multiple ML models (VAD and Whisper model) and buffering logic. I will apply the concept of Ray's Deploy Compositions of Models to independently scale and configure each of the ML models and business logic.
Ray is intended for use for strict controlled network only. It is strongly recommend that you should avoid expose the Ray Dashboard service or Ray Serve service to the Internet without proper authentication and control.
- In this EKS cluster, Karpenter and KubeRay helm chart, and NodePool and EC2NodeClass custom resource will be deployed.
cd infra
./install.sh
- Edit the
PYANNOTE_AUTH_TOKENand provide a Hugging Face token in the environment variable. For best practice, store the token in a Kubernetes secret.
❯ vi Whisper-RayService.yaml
...
env_vars: {"PYANNOTE_AUTH_TOKEN": "hf_123"}
...
- Deploy the KubeRay Service.
❯ kubectl apply -f Whisper-RayService.yaml
- Check the Ray workers and Ray Serve deployments are ready:
# Check Ray workers are ready
❯ kubectl get pod
NAME READY STATUS RESTARTS AGE
isper-streaming-raycluster-c2gdq-worker-gpu-group-6vxz5 1/1 Running 0 84m
whisper-streaming-raycluster-c2gdq-head-nxt2g 2/2 Running 0 98m
# Check RayService is ready
❯ kubectl describe RayService whisper-streaming
Name: whisper-streaming
Namespace: default
API Version: ray.io/v1
Kind: RayService
Spec:
...
Status:
...
Service Status: Running
# Get the service names
❯ kubectl get svc
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
whisper-streaming-head-svc ClusterIP 172.20.146.174 <none> 10001/TCP,8265/TCP,52365/TCP,6379/TCP,8080/TCP,8000/TCP 5d5h
whisper-streaming-raycluster-c2gdq-head-svc ClusterIP 172.20.89.123 <none> 10001/TCP,8265/TCP,52365/TCP,6379/TCP,8080/TCP,8000/TCP 98m
whisper-streaming-serve-svc ClusterIP 172.20.191.110 <none> 8000/TCP 5d5h
You can access the Ray Dashboard after port-forwarding 8265.
kubectl port-forward svc/whisper-streaming-head-svc 8265:8265
open http://localhost:8265
The ASR service is exposed as a WebSocket service. You can port-forward the service 8000 and send live audio stream via a browser.
kubectl port-forward svc/whisper-streaming-serve-svc 8000:8000
open VoiceStreamAI_Client.html
Simulate 20 audio streams with Locust using the command. With Ray Serve Autoscaler, you are able to serve ML models that scales out and in according to the request count automatically.
❯ locust -u 20 --headless -f locustfile.py
[2024-03-15 12:52:36,101] bcd07456717e/INFO/locust.main: No run time limit set, use CTRL+C to interrupt
[2024-03-15 12:52:36,101] bcd07456717e/INFO/locust.main: Starting Locust 2.22.0
Type Name # reqs # fails | Avg Min Max Med | req/s failures/s
--------|----------------------------------------------------------------------------|-------|-------------|-------|-------|-------|-------|--------|-----------
--------|----------------------------------------------------------------------------|-------|-------------|-------|-------|-------|-------|--------|-----------
Aggregated 0 0(0.00%) | 0 0 0 0 | 0.00 0.00
[2024-03-15 12:52:36,101] bcd07456717e/INFO/locust.runners: Ramping to 20 users at a rate of 1.00 per second
[2024-03-15 12:52:36,228] bcd07456717e/INFO/root: Loading audio file
[2024-03-15 12:52:36,230] bcd07456717e/INFO/root: Start sending audio
[2024-03-15 12:52:37,280] bcd07456717e/INFO/root: Loading audio file
[2024-03-15 12:52:37,281] bcd07456717e/INFO/root: Start sending audio
Type Name # reqs # fails | Avg Min Max Med | req/s failures/s
--------|----------------------------------------------------------------------------|-------|-------------|-------|-------|-------|-------|--------|-----------
[Send] Audio trunks 10 0(0.00%) | 255 252 259 259 | 0.00 0.00
[Connect] Websocket 2 0(0.00%) | 150 125 174 130 | 0.00 0.00
--------|----------------------------------------------------------------------------|-------|-------------|-------|-------|-------|-------|--------|-----------
Aggregated 12 0(0.00%) | 238 125 259 250 | 0.00 0.00
[2024-03-15 12:52:38,235] bcd07456717e/INFO/root: Loading audio file
[2024-03-15 12:52:38,236] bcd07456717e/INFO/root: Start sending audio
[2024-03-15 12:52:39,247] bcd07456717e/INFO/root: Loading audio file
[2024-03-15 12:52:39,249] bcd07456717e/INFO/root: Start sending audio
[2024-03-15 12:52:39,830] bcd07456717e/INFO/root: {"language": "en", "language_probability": 0.94970703125, "text": "Good morning, everyone.", "words": [{"word": " Good", "start": 0.0, "end": 0.9, "probability": 0.93701171875}, {"word": " morning,", "start": 0.9, "end": 1.22, "probability": 0.9697265625}, {"word": " everyone.", "start": 1.34, "end": 1.7, "probability": 0.99462890625}], "processing_time": 0.4552152156829834}
Type Name # reqs # fails | Avg Min Max Med | req/s failures/s
--------|----------------------------------------------------------------------------|-------|-------------|-------|-------|-------|-------|--------|-----------
[Send] Audio trunks 36 0(0.00%) | 255 251 264 260 | 4.00 0.00
[Receive] Response 1 0(0.00%) | 0 0 0 0 | 0.00 0.00
[Connect] Websocket 4 0(0.00%) | 142 125 174 130 | 1.00 0.00
--------|----------------------------------------------------------------------------|-------|-------------|-------|-------|-------|-------|--------|-----------
Aggregated 41 0(0.00%) | 238 0 264 260 | 5.00 0.00
[2024-03-15 12:52:40,238] bcd07456717e/INFO/root: Loading audio file
[2024-03-15 12:52:40,239] bcd07456717e/INFO/root: Start sending audio
[2024-03-15 12:52:40,856] bcd07456717e/INFO/root: {"language": "en", "language_probability": 0.94970703125, "text": "Good morning, everyone.", "words": [{"word": " Good", "start": 0.0, "end": 0.9, "probability": 0.93701171875}, {"word": " morning,", "start": 0.9, "end": 1.22, "probability": 0.9697265625}, {"word": " everyone.", "start": 1.34, "end": 1.7, "probability": 0.99462890625}], "processing_time": 0.45716142654418945}
Follow the docs - Using Prometheus and Grafana to deploy Prometheus and Grafana to build Dashboard for Ray Cluster.
- [ASR Core] The latency is high because the audio is segmented by VAD or silence. In other words, the implementation is not real time yet. Refer to the 3. Create a Streaming ASR Demo with Transformers for real time streaming ASR as future work.
- [General] The code of testing are not modified / implemented.
- [Cold Start] Store ML models on S3 storage instead of download from Internet
- [Cold Start] Pre-load ML container images in the data volume of Bottlerocket OS instead of downloading from ECR each time.
- [Cost] After ASR streaming is optimized, evaluate the performance and cost
This library is licensed under the MIT-0 License. See the LICENSE file.