Command-line Clients
Contents
Command-line Clients#
Data Center
The Riva Speech Docker image contains sample command-line drivers for the Riva Speech AI services. Pull and run the container by using the following commands. The client expects that a Riva server is running with models deployed, and all command-line drivers accept an optional argument to specify the location of the server. No GPU is required to run the sample clients.
docker pull nvcr.io/nvidia/riva/riva-speech:2.8.0
docker run -it --rm nvcr.io/nvidia/riva/riva-speech:2.8.0
Embedded
The sample command-line clients are present in the Riva Speech AI server container. Refer to the Quick Start Guide for steps on how to launch the Riva Speech AI server container.
Speech Recognition#
Both binary and Python clients are included in the Docker image with options to perform ASR inference in streaming as well as offline (nonstreaming) mode.
Binary Streaming Example#
Run the ASR streaming client, where --audio_file specifies the
audio file that is to be transcribed. Other options can be found in
riva_streaming_asr_client --help.
riva_streaming_asr_client --audio_file /opt/riva/wav/test/1272-135031-0001.wav
Binary Offline/Batch (nonstreaming) Example#
Run the binary ASR offline client, where --audio_file specifies the
audio file that is to be transcribed. Other options can be found in
riva_asr_client --help.
riva_asr_client --audio_file /opt/riva/wav/test/1272-135031-0001.wav
Python Streaming Example#
Run the Python ASR streaming client, where --input-file specifies the
audio file that is to be transcribed. Other options can be found in the
riva_streaming_asr_client.py script.
python riva_streaming_asr_client.py --input-file=/opt/riva/wav/test/1272-135031-0001.wav
The transcribe_mic.py example is the preferred,
cross-platform way to interact with Riva. Additionally, using a microphone from an arbitrary remote system
running on any operating system and hardware platform is recommended.
python transcribe_mic.py --input-device <device_id>
Where the <device_id> of your audio input device can be obtained from:
python transcribe_mic.py --list-devices
Other Python ASR clients are also available.
transcribe_file.py transcribes audio files in streaming mode.
transcribe_file_offline.py transcribes audio files in offline mode. You can play audio while transcribing using PyAudio.
Binary Offline Speaker Diarization Example#
Run the binary ASR offline client, where --audio_file specifies the audio file to be diarized and --speaker_diarization is the flag to enable diarization.
riva_asr_client --audio_file /opt/riva/wav/en-US_sample.wav --speaker_diarization=true
Python Offline Speaker Diarization Example#
Run the Python ASR offline client, where --input-file specifies the audio file to be diarized and --speaker-diarization is the flag to enable diarization.
python transcribe_file_offline.py --input-file /opt/riva/wav/en-US_sample.wav --speaker-diarization
Natural Language Processing#
Both binary and Python clients are included in the Docker image for supported NLP tasks.
Binary NER Example#
Run the Token Classification Model (NER), where --queries specifies the
query file in which to provide token labels for. Other options can be found in
riva_nlp_classify_tokens --help.
riva_nlp_classify_tokens --queries=/work/test_files/nlu/queries.txt
Binary QA Example#
Run the binary Question Answering client, where --questions specifies the
file containing questions and --contexts specifies the file containing the context
paragraph. Other options can be found in riva_nlp_qa --help.
riva_nlp_qa --questions=/work/test_files/nlu/qa_questions.txt --contexts=/work/test_files/nlu/qa_contexts.txt
Python QA Example#
Run the Python Question Answering
client.
Provide --context and --query in the test_qa.py script to try different Question Answering examples.
python qa_client.py
Python Intent Slot Example#
Run the Python Intent Slot Classification
client, where --query
specifies the query string.
python intentslot_client.py --query "your query" --model riva_intent_weather
Binary Punctuation Example#
Run the Punctuation client, where --queries specifies the file containing
inputs to punctuate. Note that if you want to specify --output to write output of the
punctuation model to a file, you need to also specify --parallel_requests=1.
riva_nlp_punct --queries=/work/test_files/nlu/punctuation_input.txt
Speech Synthesis#
Binary clients are supported in the Docker image for TTS.
Binary TTS Client Example#
Run the binary TTS client, where --text is used to specify the input text for which to
synthesize audio. The output of the client is a .wav file, which can be specified by --audio_file. Other options can be
found in riva_tts_client --help.
riva_tts_client --text="I had a dream yesterday." --audio_file=/opt/riva/wav/output.wav
Binary TTS Performance Client Example#
Run the binary TTS performance client, which provides information about latency and
throughput. Options --text specifies the input text and --text_file
specifies the file containing multiple text inputs. Other options can be found in
riva_tts_perf_client --help.
riva_tts_perf_client --text_file=/work/test_files/tts/ljs_audio_text_test_filelist_small.txt
Python Client Examples#
The talk.py script is an implementation of a client for performing offline and streaming (with audio being streamed
back to the client in chunks) inference.
python talk.py --stream --output-device <device_id>
where the <device_id> of your audio output device can be obtained from:
python talk.py --list-devices