Model Overview

Squeezeformer X-Small model which has been trained on the Librispeech 960 hours corpus.

It utilizes a Google SentencePiece [1] tokenizer with vocabulary size 128, and transcribes text in lower case english alphabet along with spaces, apostrophes and a few other characters.

Model Architecture

Squeezeformer CTC [2] model is an non-autoregressive, connectionist-temporal-classification based Automatic Speech Recognition model. You may find more info on the detail of this model here: Squeezeformer Model.

Training

The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this example script and this base config.

The tokenizers for these models were built using the text transcripts of the train set with this script.

Datasets

All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech:

LibriSpeech 960 hours of English speech

Tokenizer Construction

The tokenizer for this model was built using text corpus provided with the train dataset.

We build a Google Sentencepiece Tokenizer [1] with the following script :

" \\\n --vocab_size=128 \\\n --tokenizer="spe" \\\n --spe_type="unigram" \\\n --spe_character_coverage=1.0 \\\n --no_lower_case \\\n --log\n\">

python [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \\\n  --manifest=\"train_manifest.json\" \\\n  --data_root=\"<OUTPUT DIRECTORY FOR TOKENIZER>\" \\\n  --vocab_size=128 \\\n  --tokenizer=\"spe\" \\\n  --spe_type=\"unigram\" \\\n  --spe_character_coverage=1.0 \\\n  --no_lower_case \\\n  --log\n

Performance

The performance of Automatic Speech Recognition models is measuring using Word Error Rate.

The model obtains the following scores on the following evaluation datasets -

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Version	Tokenizer	Vocabulary Size	LS dev-clean	LS test-clean	LS dev-other	LS test-other	Train Dataset
1.13.0	Google Sentencepiece	128	3.6	3.8	9.7	9.4	Librispeech 960 h

Note that these scores on Librispeech are not particularly indicative of the quality of transcriptions that models trained on ASR Set will achieve, but they are a useful proxy.

How to Use this Model

The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically load the model from NGC

import nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_en_squeezeformer_ctc_xsmall_ls\")\n

Transcribing text with this model

"\n\">

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n  pretrained_name=\"stt_en_squeezeformer_ctc_xsmall_ls\" \\\n  audio_dir=\"<DIRECTORY CONTAINING AUDIO FILES>\"\n

Input

This model accepts 16000 KHz Mono-channel Audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

Limitations

Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.

References

[1] Google Sentencepiece Tokenizer

[2] Squeezeformer: An Efficient Transformer for Automatic Speech Recognition

[3] NVIDIA NeMo Toolkit

Licence

License to use this model is covered by the CC-BY-4 License unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-4 License.

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You may find more info on the detail of this model here: [Squeezeformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html).\n\n## Training\n\nThe NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/squeezeformer/squeezeformer_ctc_bpe.yaml).\n\nThe tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).\n\n### Datasets\n\nAll the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech:\n\n- LibriSpeech 960 hours of English speech\n\n### Tokenizer Construction\n\nThe tokenizer for this model was built using text corpus provided with the train dataset.\n\nWe build a Google Sentencepiece Tokenizer [1] with the following script :\n\n```bash\npython [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \\\n --manifest=\"train_manifest.json\" \\\n --data_root=\"\" \\\n --vocab_size=128 \\\n --tokenizer=\"spe\" \\\n --spe_type=\"unigram\" \\\n --spe_character_coverage=1.0 \\\n --no_lower_case \\\n --log\n```\n\n## Performance\n\nThe performance of Automatic Speech Recognition models is measuring using Word Error Rate.\n\nThe model obtains the following scores on the following evaluation datasets -\n\n| Version | Tokenizer | Vocabulary Size | LS dev-clean | LS test-clean | LS dev-other | LS test-other | Train Dataset |\n|---------|----------------------|-----------------|--------------|---------------|--------------|---------------|-------------------|\n| 1.13.0 | Google Sentencepiece | 128 | 3.6 | 3.8 | 9.7 | 9.4 | Librispeech 960 h |\n\nNote that these scores on Librispeech are not particularly indicative of the quality of transcriptions that models trained on ASR Set will achieve, but they are a useful proxy.\n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n\n### Automatically load the model from NGC\n\n```python\nimport nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_en_squeezeformer_ctc_xsmall_ls\")\n```\n\n### Transcribing text with this model\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_en_squeezeformer_ctc_xsmall_ls\" \\\n audio_dir=\"\"\n```\n\n### Input\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.\n\n### Output\n\nThis model provides transcribed speech as a string for a given audio sample.\n\n## Limitations\n\nSince this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.\n\n## References\n\n[1] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\n\n[2] [Squeezeformer: An Efficient Transformer for Automatic Speech Recognition](https://arxiv.org/abs/2206.00888)\n\n[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n### Licence\n\nLicense to use this model is covered by the [CC-BY-4 License](https://creativecommons.org/licenses/by/4.0/legalcode) unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4 License](https://creativecommons.org/licenses/by/4.0/legalcode).","STT En Squeezeformer CTC XSmall Librispeech",["D",1680637042640],"/api/image/nvidia/nemo/models/stt_en_squeezeformer_ctc_xsmall_ls","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[311,312,313,314,315,316,317,318],[],[],[],{"_87":336,"_135":337,"_136":338,"_138":139},{"_87":332,"_135":333,"_136":334,"_138":139},{"_87":330,"_135":330,"_136":331,"_138":139},{"_87":328,"_135":328,"_136":329,"_138":139},{"_87":325,"_135":325,"_136":326,"_138":139},{"_87":323,"_135":323,"_136":324,"_138":139},{"_87":321,"_135":321,"_136":322,"_138":139},{"_87":319,"_135":319,"_136":320,"_138":139},"STT",{"_140":139,"_135":319},"Squeezeformer",{"_140":139,"_135":321},"PytorchLightning",{"_140":139,"_135":323},"PyTorch",{"_140":327,"_135":325},"framework","NeMo",{"_140":327,"_135":328},"En",{"_140":139,"_135":330},"Conversational AI","soln_conversational_ai",{"_140":335,"_135":333},"technology","Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_140":146,"_135":337},{"_203":1314,"_205":1315},"ngcApps",[1267],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1248":1249,"_1250":1251,"_1252":1253,"_1254":1255,"_1256":350,"_234":374,"_1257":1258,"_1259":50,"_1260":180,"_1261":180,"_1262":1263,"_1264":180,"_1265":1266},"amplitude",{"_1245":1246,"_887":-7,"_1247":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1239":1240,"_1241":1242,"_1243":1244},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1127":264,"_1128":1129,"_1130":1131,"_1132":1133},"announcements",[],"consentState",{"_412":1117,"_1118":180,"_1119":26,"_1120":26,"_1121":1122},"consentConfig",{"_394":395,"_396":397,"_398":399,"_400":401,"_402":403,"_404":405,"_406":407,"_408":409,"_410":411,"_412":413},"showContactAdmin",1785675679874,[],[],[388,389,390,391,392,393],{"_39":54,"_41":63,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":61,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":59,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":55,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":60,"_45":-5,"_47":-5,"_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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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667],[614,615,616,617,618],{"_453":528,"_87":528,"_86":264,"_426":638},{"_453":502,"_87":502,"_86":264,"_426":633},{"_453":628,"_87":628,"_86":264,"_426":629},{"_453":623,"_87":623,"_86":264,"_426":624},{"_453":619,"_87":619,"_86":264,"_426":620},"nvidia.partners",[621],{"_290":622,"_459":619,"_460":26,"_86":496},"edge","app.hushly.com",[625],{"_290":626,"_459":623,"_460":26,"_86":627},"*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",[630],{"_290":631,"_459":628,"_460":26,"_86":632},"_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. Information collected with these cookies is used to measure and find ways to improve website performance.",3,[727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755],[707,708,709,710],{"_453":722,"_87":722,"_86":264,"_426":723},{"_453":717,"_87":717,"_86":264,"_426":718},{"_453":515,"_87":515,"_86":264,"_426":714},{"_453":628,"_87":628,"_86":264,"_426":711},[712],{"_290":713,"_459":628,"_460":180,"_86":632},"_gh_sess",[715],{"_290":716,"_459":515,"_460":180,"_86":522},"YSC","bizible.com",[719],{"_290":720,"_459":717,"_460":26,"_86":721},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[724],{"_290":725,"_459":722,"_460":26,"_86":726},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_290":797,"_459":574,"_460":26,"_86":798},{"_290":782,"_459":796,"_460":180,"_86":783},{"_290":765,"_459":577,"_460":26,"_86":689},{"_290":765,"_459":677,"_460":26,"_86":689},{"_290":794,"_459":574,"_460":26,"_86":795},{"_290":782,"_459":580,"_460":180,"_86":783},{"_290":792,"_459":574,"_460":26,"_86":793},{"_290":782,"_459":577,"_460":180,"_86":783},{"_290":790,"_459":676,"_460":26,"_86":791},{"_290":782,"_459":698,"_460":180,"_86":783},{"_290":762,"_459":577,"_460":26,"_86":764},{"_290":788,"_459":574,"_460":180,"_86":789},{"_290":786,"_459":574,"_460":26,"_86":787},{"_290":784,"_459":574,"_460":180,"_86":785},{"_290":782,"_459":672,"_460":180,"_86":783},{"_290":780,"_459":574,"_460":26,"_86":781},{"_290":778,"_459":574,"_460":26,"_86":779},{"_290":776,"_459":574,"_460":180,"_86":777},{"_290":774,"_459":681,"_460":26,"_86":775},{"_290":762,"_459":672,"_460":26,"_86":764},{"_290":772,"_459":574,"_460":26,"_86":773},{"_290":770,"_459":574,"_460":180,"_86":771},{"_290":768,"_459":574,"_460":26,"_86":769},{"_290":766,"_459":574,"_460":26,"_86":767},{"_290":765,"_459":574,"_460":26,"_86":689},{"_290":762,"_459":763,"_460":26,"_86":764},{"_290":760,"_459":574,"_460":26,"_86":761},{"_290":758,"_459":574,"_460":180,"_86":759},{"_290":756,"_459":574,"_460":26,"_86":757},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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