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Changelog

January 2020

Initial release

April 2020

Printing WER accuracy in Triton client
Using the latest Kaldi GPU ASR pipeline, extended support for features (ivectors, fbanks)

Known issues

No multi-gpu support for the Triton integration

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This repository provides a wrapper around the online GPU-accelerated ASR pipeline from the paper GPU-Accelerated Viterbi Exact Lattice Decoder for Batched Online and Offline Speech Recognition. That work includes a high-performance implementation of a GPU HMM Decoder, a low-latency Neural Net driver, fast Feature Extraction for preprocessing, and new ASR pipelines tailored for GPUs. These different modules have been integrated into the Kaldi ASR framework.

This repository contains a Triton custom backend for the Kaldi ASR framework. This custom backend calls the high-performance online GPU pipeline from the Kaldi ASR framework. This Triton integration provides ease-of-use to Kaldi ASR inference: gRPC streaming server, dynamic sequence batching, and multi-instances support. A client connects to the gRPC server, streams audio by sending chunks to the server, and gets back the inferred text as an answer (see Input/Output). More information about the Triton can be found here.

This Triton integration is meant to be used with the LibriSpeech model for demonstration purposes. We include a pre-trained version of this model to allow you to easily test this work (see Quick Start Guide). Both the Triton integration and the underlying Kaldi ASR online GPU pipeline are a work in progress and will support more functionalities in the future. Support for a custom Kaldi model is experimental (see Using a custom Kaldi model).

Reference model

A reference model is used by all test scripts and benchmarks presented in this repository to illustrate this solution. We are using the Kaldi ASR LibriSpeech recipe, available here. It was trained by NVIDIA and is delivered as a pre-trained model.

Default configuration

Details about parameters can be found in the Parameters section.

model path: Configured to use the pretrained LibriSpeech model.
use_tensor_cores: 1
main_q_capacity: 30000
aux_q_capacity: 400000
beam: 10
num_channels: 4000
lattice_beam: 7
max_active: 10,000
frame_subsampling_factor: 3
acoustic_scale: 1.0
num_worker_threads: 40
max_batch_size: 400
instance_group.count: 1

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Aside from these dependencies, ensure you have [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) installed.\n\nFor more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:\n- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)\n- [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)","hasSetup","quickStartGuide","1. Clone the repository.\n \n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples.git\ncd DeepLearningExamples/Kaldi/SpeechRecognition\n```\n\n2. Build the client and server containers.\n \n`scripts/docker/build.sh`\n\n3. Download and set up the pre-trained model and eval dataset.\n\n`scripts/docker/launch_download.sh`\n\nThe model and dataset are downloaded in the `data/` folder.\n\n4. Start the server.\n\n`scripts/docker/launch_server.sh`\n\nOnce you see the line `Starting Metrics Service at 0.0.0.0:8002`, the server is ready to be used. You can then start the client.\n\nCurrently, multi-GPU is not supported. By default GPU 0 is used. You can use a specific GPU by using `NVIDIA_VISIBLE_DEVICES`:\n\n`NVIDIA_VISIBLE_DEVICES= scripts/docker/launch_server.sh`\n\n5. Start the client.\n\nThe following command will stream 1000 parallel streams to the server. The `-p` option prints the inferred `TEXT` sent back from the server. \n\n`scripts/docker/launch_client.sh -p`","hasQuickStartGuide","advanced","### Parameters\n\nThe configuration is done through the `config.pbtxt` file available in the `model-repo/kaldi_online/` directory. It allows you to specify the following:\n\n#### Model path\n\nThe following parameters can be modified if you want to use your own Kaldi model. \n\n* `mfcc_filename`\n* `ivector_filename`\n* `nnet3_rxfilename`\n* `fst_rxfilename`\n* `word_syms_rxfilename`\n\n#### Model configuration\n\nThe model configuration parameters are passed to the model and have an impact on both accuracy and performance. The model parameters are usually Kaldi ASR parameters, meaning, if they are, you can reuse the values that are currently being used in the CPU Kaldi ASR pipeline. \n\n* `beam`\n* `lattice_beam`\n* `max_active`\n* `frame_subsampling_factor`\n* `acoustic_scale`\n\n#### Inference engine configuration\n\nThe inference engine configuration parameters configure the inference engine. They impact performance, but not accuracy.\n\n* `max_batch_size`: The size of one execution batch on the GPU. This parameter should be set as large as necessary to saturate the GPU, but not bigger. Larger batches will lead to a higher throughput, smaller batches to lower latency.\n* `num_worker_threads`: The number of CPU threads for the postprocessing CPU tasks, such as lattice determinization and text generation from the lattice.\n* `input.WAV_DATA.dims`: The maximum number of samples per chunk. The value must be a multiple of `frame_subsampling_factor * chunks_per_frame`.\n\n### Inference process\n\nInference is done through simulating concurrent users. Each user is attributed to one utterance from the LibriSpeech dataset. It streams that utterance by cutting it into chunks and gets the final `TEXT` output once the final chunk has been sent. The `-c` parameter sets the number of active users being simulated in parallel. \n\n### Client command-line parameters\n\nThe client can be configured through a set of parameters that define its behavior. To see the full list of available options and their descriptions, use the `-h` command-line option. The parameters are:\n\n```\n -v\n -i \n -c \n -a \n -l \n -u \n -o : Only feed each channel at realtime speed. Simulates online clients.\n -p : Print text outputs\n -b : Print partial (best path) text outputs\n```\n\n### Input/Output\n\nThe API is currently experimental.\n\n#### Input\n\nThe server execpts chunks of audio each containing up to `input.WAV_DATA.dims` samples. Per default, this corresponds to 510ms of audio per chunk. The last chunk can send a partial chunk smaller than this maximum value. \n\nThe chunk is made of a float array set in the input `WAV_DATA`, with the input `WAV_DATA_DIM` containing the number of samples contained in that chunk. Flags can be set to declare a chunk as a first chunk or last chunk for a sequence. Finally, each chunk from a given sequence is associated with a `CorrelationID`. Every chunk belonging to the same sequence must be given the same `CorrelationID`. \n\n#### Output\n\nOnce the server receives the final chunk for a sequence (with the `END` flag set), it will generate the output associated with that sequence, and send it back to the client. The end of the sequencing procedure is:\n\n1. Process the last chunk.\n2. Flush and process the Neural Net context. \n3. Generate the full lattice for the sequence.\n4. Determinize the lattice.\n5. Find the best path in the lattice.\n6. Generate the text output for that best path.\n7. Send the text back to the client.\n\nEven if only the best path is used, we are still generating a full lattice for benchmarking purposes. Partial results (generated after each timestep) are currently not available but will be added in a future release. \n\n### Using a custom Kaldi ASR model\n\nSupport for Kaldi ASR models that are different from the provided LibriSpeech model is experimental. However, it is possible to modify the [Model Path](#model-path) section of the config file `model-repo/kaldi_online/config.pbtxt` to set up your own model.","hasAdvanced","performance","### Metrics\n\nThroughput is measured using the RTFX metric. It is defined such as : `RTFX = (number of seconds of audio inferred) / (compute time in seconds)`. It is the inverse of the RTF (Real Time Factor) metric, such as `RTFX = 1/RTF`.\n\nLatency is defined as the delay between the availability of the last chunk of audio and the reception of the inferred text. More precisely, it is defined such as :\n\n1. *Client:* Last audio chunk available\n2. ***t0** <- Current time*\n3. *Client:* Send last audio chunk\n4. *Server:* Compute inference of last chunk\n5. *Server:* Generate the raw lattice for the full utterance\n6. *Server:* Determinize the raw lattice\n8. *Client:* Receive lattice output\n9. *Client:* Call callback with output\n10. ***t1** <- Current time* \n\nThe latency is defined such as `latency = t1 - t0`.\n\n### Results\n\nOur results were obtained by:\n\n1. Building and starting the server as described in [Quick Start Guide](#quick-start-guide).\n2. Running `scripts/run_inference_all_a100.sh`, `scripts/run_inference_all_v100.sh` and `scripts/run_inference_all_t4.sh`\n\n| GPU | Realtime I/O | Number of parallel audio channels | Latency (s) | | | |\n| ----- | ------------ | --------------------------------- | ----------- | ----- | ----- | ----- |\n| | | | 90% | 95% | 99% | Avg |\n| A100 | Yes | 2000 | 0.11 | 0.12 | 0.14 | 0.09 |\n| V100 | Yes | 2000 | 0.42 | 0.50 | 0.61 | 0.23 |\n| V100 | Yes | 1000 | 0.09 | 0.09 | 0.11 | 0.07 |\n| T4 | Yes | 600 | 0.17 | 0.18 | 0.22 | 0.14 |\n| T4 | Yes | 400 | 0.12 | 0.13 | 0.15 | 0.10 |","hasPerformance","policies",[],{"_177":186,"_179":187,"_146":-5,"_154":26,"_156":26,"_170":185,"_157":66},{"_177":184,"_179":184,"_146":-5,"_154":26,"_156":26,"_170":185,"_157":66},{"_177":14,"_179":14,"_146":-5,"_154":26,"_156":26,"_170":183,"_157":66},{"_177":181,"_179":181,"_146":-5,"_154":26,"_156":26,"_170":182,"_157":66},{"_177":178,"_179":178,"_146":-5,"_154":26,"_156":26,"_170":180,"_157":66},"id","20.03.0","name",[],"19.12.0",[],[],"21.05.0",[],"-","Latest (21.05.0)","nvidia/kaldi","key","nvidia/resources/kaldi","resourceId","kaldi","shortDescription","Kaldi ASR custom backend for the NVIDIA Triton Inference Server.","This repository provides a wrapper around the online GPU-accelerated ASR pipeline from the paper [GPU-Accelerated Viterbi Exact Lattice Decoder for Batched Online and Offline Speech Recognition](https://arxiv.org/abs/1910.10032). That work includes a high-performance implementation of a GPU HMM Decoder, a low-latency Neural Net driver, fast Feature Extraction for preprocessing, and new ASR pipelines tailored for GPUs. These different modules have been integrated into the Kaldi ASR framework.\n\nThis repository contains a Triton custom backend for the Kaldi ASR framework. This custom backend calls the high-performance online GPU pipeline from the Kaldi ASR framework. This Triton integration provides ease-of-use to Kaldi ASR inference: gRPC streaming server, dynamic sequence batching, and multi-instances support. A client connects to the gRPC server, streams audio by sending chunks to the server, and gets back the inferred text as an answer (see [Input/Output](#input-output)). More information about the Triton can be found [here](https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/). \n\nThis Triton integration is meant to be used with the LibriSpeech model for demonstration purposes. We include a pre-trained version of this model to allow you to easily test this work (see [Quick Start Guide](#quick-start-guide)). Both the Triton integration and the underlying Kaldi ASR online GPU pipeline are a work in progress and will support more functionalities in the future. Support for a custom Kaldi model is experimental (see [Using a custom Kaldi model](#using-custom-kaldi-model)).\n\n### Reference model\n\nA reference model is used by all test scripts and benchmarks presented in this repository to illustrate this solution. We are using the Kaldi ASR `LibriSpeech` recipe, available [here](https://github.com/kaldi-asr/kaldi/blob/master/egs/librispeech/s5). It was trained by NVIDIA and is delivered as a pre-trained model.\n\n### Default configuration\n\nDetails about parameters can be found in the [Parameters](#parameters) section.\n\n* `model path`: Configured to use the pretrained LibriSpeech model.\n* `use_tensor_cores`: 1\n* `main_q_capacity`: 30000\n* `aux_q_capacity`: 400000\n* `beam`: 10\n* `num_channels`: 4000\n* `lattice_beam`: 7\n* `max_active`: 10,000\n* `frame_subsampling_factor`: 3\n* `acoustic_scale`: 1.0\n* `num_worker_threads`: 40\n* `max_batch_size`: 400\n* `instance_group.count`: 1","displayName","Kaldi ASR Integration With Triton Inference Server",["D",1680632530891],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/kaldi","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes",1267703,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[229,230,231,232,233],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_196":254,"_235":255,"_237":256,"_239":240},{"_196":250,"_235":251,"_237":252,"_239":240},{"_196":246,"_235":247,"_237":248,"_239":240},{"_196":243,"_235":244,"_237":245,"_239":240},{"_196":234,"_235":236,"_237":238,"_239":240},"Natural Language Understanding","value","uscs_natural_language_understanding","filter",{"_241":242,"_235":236},"category","label","field","application","Natural Language Processing","uscs_natural_language_processing",{"_241":242,"_235":244},"Deep Learning Examples","pltfm_deep_learning_examples",{"_241":249,"_235":247},"platform","Conversational AI","soln_conversational_ai",{"_241":253,"_235":251},"technology","AI","soln_ai",{"_241":253,"_235":255},{"_86":1241,"_88":1242},"ngcApps",[1194],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1175":1176,"_1177":1178,"_1179":1180,"_1181":1182,"_1183":268,"_117":296,"_1184":1185,"_1186":50,"_1187":66,"_1188":66,"_1189":1190,"_1191":66,"_1192":1193},"amplitude",{"_1172":1173,"_810":-7,"_1174":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1166":1167,"_1168":1169,"_1170":1171},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786616706661,"1.11.0","defaultSearchSuggestions",{"_1050":377,"_1051":1052,"_1053":1054,"_1055":1056},"announcements",[],"consentState",{"_334":1040,"_1041":26,"_1042":26,"_1043":66,"_1044":1045},"consentConfig",{"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335},"showContactAdmin",1786616706662,[],[],[310,311,312,313,314,315],{"_39":54,"_41":63,"_43":56,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":61,"_43":56,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":57,"_43":56,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":55,"_43":56,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":109,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1032":1033,"_1034":1035,"_1036":1037,"_1038":1039},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[336,337,338,339,340],{"_341":721,"_179":722,"_144":723,"_345":66,"_346":724,"_348":725,"_350":726},{"_341":624,"_179":625,"_144":626,"_345":26,"_346":53,"_348":627,"_350":628},{"_341":532,"_179":533,"_144":534,"_345":26,"_346":109,"_348":535,"_350":536},{"_341":352,"_179":353,"_144":354,"_345":26,"_346":355,"_348":356,"_350":357},{"_341":342,"_179":343,"_144":344,"_345":26,"_346":347,"_348":349,"_350":351},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495],[358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374],{"_375":469,"_196":469,"_144":377,"_348":470},{"_375":465,"_196":465,"_144":377,"_348":466},{"_375":460,"_196":460,"_144":377,"_348":461},{"_375":456,"_196":456,"_144":377,"_348":457},{"_375":451,"_196":451,"_144":377,"_348":452},{"_375":446,"_196":446,"_144":377,"_348":447},{"_375":438,"_196":438,"_144":377,"_348":439},{"_375":430,"_196":430,"_144":377,"_348":431},{"_375":425,"_196":425,"_144":377,"_348":426},{"_375":420,"_196":420,"_144":377,"_348":421},{"_375":415,"_196":415,"_144":377,"_348":416},{"_375":410,"_196":410,"_144":377,"_348":411},{"_375":405,"_196":405,"_144":377,"_348":406},{"_375":398,"_196":398,"_144":377,"_348":399},{"_375":393,"_196":393,"_144":377,"_348":394},{"_375":386,"_196":386,"_144":377,"_348":387},{"_375":376,"_196":376,"_144":377,"_348":378},"hostName","amazon-adsystem.com","",[379,380],{"_179":385,"_382":376,"_383":26,"_144":384},{"_179":381,"_382":376,"_383":26,"_144":384},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[388,389],{"_179":392,"_382":386,"_383":26,"_144":391},{"_179":390,"_382":386,"_383":26,"_144":391},"IDE","This domain is owned by Doubleclick (Google). 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If you do not allow these cookies, then some or all of these services may not function properly.",[566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590],[537,538,539,540,541],{"_375":451,"_196":451,"_144":377,"_348":561},{"_375":425,"_196":425,"_144":377,"_348":556},{"_375":551,"_196":551,"_144":377,"_348":552},{"_375":546,"_196":546,"_144":377,"_348":547},{"_375":542,"_196":542,"_144":377,"_348":543},"nvidia.partners",[544],{"_179":545,"_382":542,"_383":26,"_144":419},"edge","app.hushly.com",[548],{"_179":549,"_382":546,"_383":26,"_144":550},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[553],{"_179":554,"_382":551,"_383":26,"_144":555},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. 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