Implements a full asynchronous HTTP server for converting speech to text in realtime using DeepSpeech.
Since version 0.2, DeepSpeech has supported streaming APIs that allow processing audio data in small chunks instead of all at once. This enables the capability of a real-time streaming server (given sufficient hardware).
Real time streaming of audio data requires that both client and server support HTTP chunks so that data can be passed from the client to the server in small batches. This allows the server to start processing the data with DeepSpeech while more data is being recorded and transferred by the client.
This project uses Pipenv to manage dependencies. To install the dependencies
and get a shell, run: pipenv install && pipenv shell
Once in that, shell, simply run ./app.py to start the server.
The preliminary results from this implementation are very encouraging. On my dual-core i5-6200 laptop, the server is easily able to keep up with a realtime stream of audio data from the microphone:
Client:
$ ./examples/mic http://localhost:8080/stt
rec WARN wav: Length in output .wav header will be wrong since can't seek to fix it
Input File : 'default' (pulseaudio)
Channels : 1
Sample Rate : 16000
Precision : 16-bit
Sample Encoding: 16-bit Signed Integer PCM
In:0.00% 00:00:05.63 [00:00:00.00] Out:55.8k [ | ] Clip:0
Done.
the quick brown fox jumped over the lazy dog
Server
$ ./app.py
INFO:root:Loading model...
TensorFlow: v1.12.0-10-ge232881
DeepSpeech: v0.4.1-0-g0e40db6
2019-06-05 09:54:49.098465: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
INFO:root:Model was loaded in 0.236s
======== Running on http://0.0.0.0:8080 ========
(Press CTRL+C to quit)
INFO:root:Processing Stream...
INFO:root:Inference took 1.751s for 3.486s audio sample with 0.249s latency. Total time: 7.098s
As can be seen, the server was easily able to process the streamed audio data about twice as fast as it was streamed. In addition, the minimal latency (time from when the last audio sample was received to the time when the server sent a response to the client) meant that the server was able to respond back to the client very promptly once the audio clip was finished.