NVIDIA Riva is a GPU-accelerated SDK for building Speech AI applications that are customized for your use case and deliver real-time performance. This repo provides performant client example command-line clients.
Main API
riva.client.ASRService is a class for speech recognition,
riva.client.TTSService is a class for speech synthesis,
riva.client.NLPService is a class for natural language processing.
CLI interface
Automatic Speech Recognition (ASR)scripts/asr/riva_streaming_asr_client.py demonstrates streaming transcription in several threads, can print time stamps.
scripts/asr/transcribe_file.py performs streaming transcription,
scripts/asr/transcribe_file_offline.py performs offline transcription,
scripts/asr/transcribe_mic.py performs streaming transcription of audio acquired through microphone.
scripts/asr/realtime_asr_client.py performs realtime transcription of audio via WebSocket connection.
Speech Synthesis (TTS)scripts/tts/talk.py synthesizes audio for a text in streaming or offline mode.
scripts/tts/realtime_tts_client.py performs realtime synthesis of text via WebSocket connection.
Natural Language Processing (NLP)scripts/nlp/intentslot_client.py recognizes intents and slots in input sentences,
scripts/nlp/ner_client.py detects named entities in input sentences,
scripts/nlp/punctuation_client.py restores punctuation and capitalization in input sentences,
scripts/nlp/qa_client.py queries a document with natural language query and prints answer from a document,
scripts/nlp/text_classify_client.py classifies input sentences,
scripts/nlp/eval_intent_slot.py prints intents and slots classification reports for test data.
Installation
Create a conda environment and activate it
From source: Clone riva-python-clients repo and change to the repo root
Run commands
git clone https://github.com/nvidia-riva/python-clients.git cd python-clients git submodule init git submodule update --remote --recursive pip install -r requirements.txt python3 setup.py bdist_wheel pip install --force-reinstall dist/*.whlpip:
pip install nvidia-riva-clientIf you would like to use output and input audio devices (scripts scripts/asr/transcribe_file_rt.py, scripts/asr/transcribe_mic.py, scripts/tts/talk.py, scripts/asr/realtime_asr_client.py, scripts/tts/realtime_tts_client.py or module riva.client/audio_io.py), you will need to install PyAudio.
conda install -c anaconda pyaudioIf you would like to use Realtime ASR or TTS (WebSocket-based real-time transcription or synthesis) scripts scripts/asr/realtime_asr_client.py or scripts/tts/realtime_tts_client.py, you will need the following dependencies:
conda install -c anaconda numpy conda install -c anaconda requests conda install -c anaconda websocketsFor NLP evaluation you will need transformers and sklearn libraries.
pip install -U scikit-learn pip install -U transformersBefore using microphone and audio output devices on Unix
you may need to run commands
adduser $USER audio adduser $USER pulse-access and restart.
Usage
Server
Before running client part of Riva, please set up a server. The simplest way to do this is to follow
.
CLI
You may find all CLI scripts in scripts directory. Each script has a description of its purpose and parameters.
ASR
You may find a detailed documentation
.
For transcribing in streaming mode you may use scripts/asr/transcribe_file.py.
python scripts/asr/transcribe_file.py \ --input-file data/examples/en-US_AntiBERTa_for_word_boosting_testing.wavYou may watch how a transcript grows if you set --simulate-realtime and --show-intermediate.
python scripts/asr/transcribe_file.py \ --input-file data/examples/en-US_AntiBERTa_for_word_boosting_testing.wav \ --simulate-realtime \ --show-intermediateYou may listen audio simultaneously with transcribing (you will need installed PyAudio and access to audio devices).
python scripts/asr/transcribe_file.py \ --input-file data/examples/en-US_AntiBERTa_for_word_boosting_testing.wav \ --play-audio \ --show-intermediateOffline transcription is performed this way.
python scripts/asr/transcribe_file_offline.py \ --input-file data/examples/en-US_AntiBERTa_for_word_boosting_testing.wavYou can improve transcription of this audio by word boosting.
python scripts/asr/transcribe_file_offline.py \ --input-file data/examples/en-US_AntiBERTa_for_word_boosting_testing.wav \ --boosted-lm-words AntiBERTa \ --boosted-lm-words ABlooper \ --boosted-lm-score 20.0For transcribing in realtime mode you may use scripts/asr/realtime_asr_client.py.
From audio file:
python scripts/asr/realtime_asr_client.py \ --input-file data/examples/en-US_AntiBERTa_for_word_boosting_testing.wavFrom microphone:
python scripts/asr/realtime_asr_client.py \ --mic \ --duration 30 \ --output-text transcript.txtList available audio devices:
python scripts/asr/realtime_asr_client.py --list-devicesUse specific audio device:
python scripts/asr/realtime_asr_client.py \ --mic \ --input-device 1 \ --duration 30 \ --output-text transcript.txtNLP
You can provide inputs to scripts/nlp/intentslot_client.py, scripts/nlp/punctuation_client.py both through command line arguments and interactively.
python scripts/nlp/intentslot_client.py --query "What is the weather tomorrow?"or
python scripts/nlp/intentslot_client.py --interactiveFor punctuation client the commands look similar.
python scripts/nlp/punctuation_client.py --query "can you prove that you are self aware"or
python scripts/nlp/punctuation_client.py --interactiveNER client can output 1 of the following: label name, span start, span end
python scripts/nlp/ner_client.py \ --query "Where is San Francisco?""Jensen Huang is the CEO of NVIDIA Corporation." \ --test labelor
python scripts/nlp/ner_client.py \ --query "Where is San Francisco?""Jensen Huang is the CEO of NVIDIA Corporation." \ --test span_startor
python scripts/nlp/ner_client.py \ --query "Where is San Francisco?""Jensen Huang is the CEO of NVIDIA Corporation." \ --test span_endProvide query and context to QA client.
python scripts/nlp/qa_client.py \ --query "How many gigatons of carbon dioxide was released in 2005?" \ --context "In 2010 the Amazon rainforest experienced another severe drought, in some ways "\ "more extreme than the 2005 drought. The affected region was approximate 1,160,000 square "\ "miles (3,000,000 km2) of rainforest, compared to 734,000 square miles (1,900,000 km2) in "\ "2005. The 2010 drought had three epicenters where vegetation died off, whereas in 2005 the "\ "drought was focused on the southwestern part. The findings were published in the journal "\ "Science. In a typical year the Amazon absorbs 1.5 gigatons of carbon dioxide; during 2005 "\ "instead 5 gigatons were released and in 2010 8 gigatons were released."Text classification requires only a query.
python scripts/nlp/text_classify_client.py --query "How much sun does california get?"TTS
Call scripts/tts/talk.py script, and you will be prompted to enter a text for speech synthesis. Set --play-audio option, and a synthesized speech will be played.
python scripts/tts/talk.py --play-audioYou can write output to file.
python scripts/tts/talk.py --output 'my_synth_speech.wav'You can use streaming mode (audio fragments returned to client as soon as they are ready).
python scripts/tts/talk.py --stream --play-audioFor synthesizing in realtime mode you may use scripts/tts/realtime_tts_client.py.
Direct text input:
python scripts/tts/realtime_tts_client.py \ --text "Hello, this is a text to speech example." \ --play-audioFrom text file:
python scripts/tts/realtime_tts_client.py \ --input-file input.txt \ --output output.wavList available voices:
python scripts/tts/realtime_tts_client.py --list-voicesList available audio devices:
python scripts/tts/realtime_tts_client.py --list-devicesUse specific voice and language:
python scripts/tts/realtime_tts_client.py \ --text "Hello world" \ --language-code en-US \ --voice English-US.Female-1 \ --output output.wav \ --play-audioAPI
See tutorial notebooks in directory tutorials.
Documentation
Additional documentation on the Riva Speech Skills SDK can be found
.
License
This client code is MIT-licensed. See LICENSE file for full details.