verbit-streaming-sdk
Advanced tools
This package is a Python SDK for Verbit's Streaming Transcription API. It can also be used as a reference implementation for porting to other programming languages. You can use it as-is (see installation instructions below), or use it as an example for implementing your own custom client.
The package includes a Python client for streaming media and getting responses from Verbit's Streaming Speech Recognition services via a WebSocket connection.
To install this package run:
pip install --upgrade verbit-streaming-sdk
To access Verbit's Streaming Speech Recognition services an API key (customer token) should be obtained by sending an email request to the following address: api@verbit.ai
In order to use Verbit's Streaming Speech Recognition services, you should place an order using Verbit's Ordering API. Your request to the Ordering API must specify that the desired input and/or output schemes are streaming through a WebSocket. Upon successful placement of the order, you will be issued a WebScoket URL, composed of the base streaming API URL and your order's token. The URL, together with the customer token, will be used to initiate a WebSocket connection.
These two APIs and their respective SDKs are separated on purpose because placing orders to Verbit's Transcription services does not necessarily imply media streaming (you might want to upload a file instead). Also, the services which operate order placement and the actual streaming of media are commonly distinct, therefore we find it useful to separate the SDKs to allow maximal flexibility for our customers.
For further details regarding the Ordering API, please refer to the documentation here: Ordering API.
It is also possible to connect to Verbit's Streaming Speech Recognition service without placing an order in advance. To do that, simply omit the ws_url parameter when calling start_stream.
Ad-hoc connections are supported only when streaming the media via the WebSocket; i.e. they are not supported when streaming the media from an external source.
Create the client, and pass in the Customer Token as detailed above:
from verbit.streaming_client import WebSocketStreamingClient
client = WebSocketStreamingClient(customer_token="CUSTOMER TOKEN")
Create a generator function which yields chunks of audio (objects supporting the bytes-like interface).
The WebSocketStreamingClient will use your generator as input, iterating it and sending each audio chunk to the Speech Recognition service.
Important: The Speech Recognition service expects the audio chunks to arrive at a realtime pace, or slower. Faster than realtime pace may cause the service to behave unexpectedly.
The following example reads audio from a WAV file and streams it to the Speech Recognition Service (Note: the example assumes that the customer token and WebSocket URL have been obtained via their respective API calls):
import wave
from math import ceil
from time import sleep
from verbit.streaming_client import WebSocketStreamingClient, MediaConfig, ResponseType
CHUNK_DURATION_SECONDS = 0.1
AUDIO_FILENAME = 'example.wav'
def media_generator_wavefile(filename, chunk_duration):
"""
Example generator, for streaming a 'WAV' audio-file, simulating realtime playback-rate using sleep()
"""
with wave.open(str(filename), 'rb') as wav:
nchannels, samplewidth, sample_rate, nframes, _, _ = wav.getparams()
samples_per_chunk = ceil(chunk_duration * sample_rate)
chunk_bytes = wav.readframes(samples_per_chunk)
while chunk_bytes:
yield chunk_bytes
chunk_bytes = wav.readframes(samples_per_chunk)
sleep(chunk_duration)
media_generator = media_generator_wavefile(AUDIO_FILENAME, CHUNK_DURATION_SECONDS)
media_config = MediaConfig(format='S16LE', # signed 16-bit little-endian PCM
num_channels=1, # number of audio channels
sample_rate=16000, # in Hz
sample_width=2) # in bytes
response_types = ResponseType.Transcript | ResponseType.Captions
client = WebSocketStreamingClient(customer_token="CUSTOMER TOKEN")
response_generator = client.start_stream(
ws_url="WEBSOCKET URL",
media_generator=media_generator,
media_config=media_config,
response_types=response_types)
It is possible to use an external media source to provide media to the Speech Recognition Service. To do so, you need to specify the relevant input method when booking the session via Verbit's Ordering API.
In such a scenario, you should not provide a media generator to the WebSocketStreamingClient.
Connecting the WebSocketStreamingClient to the Speech Recognition Service will initiate the session
and signal the server to start consuming media from the external media source.
Therefore, you should only connect the WebSocketStreamingClient to the service after the external media source is ready.
The following example connects to the Speech Recognition Service without providing a media generator:
from verbit.streaming_client import WebSocketStreamingClient, ResponseType
response_types = ResponseType.Transcript | ResponseType.Captions
client = WebSocketStreamingClient(customer_token="CUSTOMER TOKEN")
response_generator = client.start_with_external_source(ws_url="WEBSOCKET URL", response_types=response_types)
The client's start_stream() and start_with_external_source() methods return a generator which can be iterated to fetch the Speech Recognition responses:
# get recognition responses
print('Waiting for responses ...')
for response in response_generator:
resp_type = response['response']['type']
alternatives = response['response']['alternatives']
alt0_transcript = alternatives[0]['transcript']
print(f'{resp_type}: {alt0_transcript}')
When the media generator is exhausted, the client sends an End-of-Stream (non-binary) message to the service.
In a scenario where the media is coming from an external source, it is the user's responsibility to send the End-of-Stream message to the service.
The End-of-Stream message can be sent using the send_eos_event() method which internally sends the following payload:
{
"event": "EOS"
}
Responses received through the WebSocket are JSON objects with a specific schema (a full description of which can be found in examples/responses/schema.md). There are two types of responses - "transcript" and "captions":
Transcript: this type of response contains the recognized words since the beginning of the current utterance. Like in real human speech, the stream of words is segmented into utterances in automatic speech recognition. An utterance is recognized incrementally, processing more of the incoming audio at each step. Each utterance starts at a specific start-time and extends its end-time with each step, yielding the most updated result.
Note that sequential updates for the same utterance will overlap, each response superseding the previous one - until a response signaling the end of the utterance is received (having is_final == True).
The alternatives array might contain different hypotheses, ordered by confidence level.
Example "transcript" responses can be found in examples/responses/transcript.md.
Captions: this type of response contains the recognized words within a specific time window. In contrast to the incremental nature of "transcript"-type responses, the "captions"-type responses are non-overlapping and consecutive.
Only one "captions"-type response covering a specific time-span in the audio will be returned (or none, if no words were uttered).
The is_final field is always True because no updates will be output for the same time-span. The alternatives array will always have only one item for captions.
Example "captions" responses can be found in examples/responses/captions.md.
It should be noted that "transcript" and "captions" responses behave differently when the audio being transcribed is silent:
The received responses may originate in one of two types of service:
Note: Due to natural differences between languages, translated responses may diverge in word count and word order. Since translated words were never really uttered in the original audio, they do not have "real" timings. Therefore, words in translation responses are assigned timings which are heuristically distributed within the time boundaries of the source language utterance. Heuristic timings may be used for synchronization purposes like displaying translated content in alignment with the media.
In case the WebSocket client fails to establish the initial connection with the service, e.g. due to temporary unavailability,
it will perform exponential retry, up to max_connection_retry_seconds (configurable).
In case the connection to the service is dropped during a session, the behavior of the WebSocket client will depend on the implementation chosen by the user. This client SDK contains two implementations, which have the same interface, but differ in their error handling behavior:
WebSocketStreamingClientSingleConnection - the base implementation; does not attempt to reconnect in case the connection was dropped prematurely. It can be useful, for example, if you would like to implement your own connection error handling logic.WebSocketStreamingClient - the default implementation; will attempt to reconnect in case the connection was closed prematurely, as many times as needed, until the final response is received (or some non-retryable error occurrs).In case the media stream comes from an external source (e.g. RTMP), there may be times when no messages are sent over the WebSocket. For example:
In case no message is sent over the WebSocket for more than 10 minutes, the connection will be dropped, and will need to be re-established. To prevent these undesired disconnections, it is advised to send a "ping" message at least once every 10 minutes. This client SDK sends a "ping" message every 1 minute, as long as the Websocket is connected.
If you choose to implement your own client, make sure to handle the "pong" messages you will get from the service, in response to your "ping" messages.
In case the media stream comes from an external source (e.g. RTMP), the maximum allowed connection duration is 2 hours. After that time, the server will drop the connection with a "Going Away" (code: 1001) close message. In such cases, it is the client's responsibility to reconnect. This client SDK automatically attempts to reconnect if connection is closed with "Going Away" message.
This client SDK comes with a set of unit-tests that can be used to ensure the correct functionality of the streaming client.
To run the unit-tests:
pip install pytest
pip install -r tests/requirements_test.txt
pytest
FAQs
Client SDK for Verbit's Streaming Speech Recognition services
We found that verbit-streaming-sdk demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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