Nemotron 3.5 ASR Streaming Multilingual 0.6B is NVIDIA’s 600-million-parameter speech-recognition model for live transcription and recorded-audio workloads. It extends the English streaming line with language-ID conditioning. Speech becomes text in the input language, with punctuation and capitalization built into the output.
The maker’s language coverage has three tiers. Nineteen transcription-ready locales cover US and UK English, US and Spain Spanish, France and Canada French, Italian, Brazil and Portugal Portuguese, Dutch, German, Turkish, Russian, Arabic, Hindi, Japanese, Korean, Vietnamese and Ukrainian. Thirteen broad-coverage locales add Polish, Swedish, Czech, Norwegian Bokmål, Danish, Bulgarian, Finnish, Croatian, Slovak, Mandarin, Hungarian, Romanian and Estonian. Together these 32 locales produce transcription without additional fine-tuning.
Eight adaptation-ready locales—Greek, Lithuanian, Latvian, Maltese, Slovenian, Hebrew, Thai and Norwegian Nynorsk—are recognized by the tokenizer but require in-domain fine-tuning to enable full transcription. Thus the producer’s 40-locale figure is not a claim that all forty have the same out-of-the-box capability. A customized checkpoint is a separate artifact from the base model.
The architecture combines a 24-layer cache-aware FastConformer encoder with an RNN-T decoder. It retains encoder attention and convolution states from earlier frames and processes each new audio chunk without repeatedly recomputing overlapping windows. A language-ID signal is supplied alongside the acoustic representation to condition the transcript or let the model infer the language.
NVIDIA’s NeMo configuration documents chunk sizes of 80, 160, 320, 560 and 1120 milliseconds. The attention setting controls how much future audio a chunk receives before text is emitted, allowing a task to balance waiting time and acoustic context. These are producer streaming configurations, not guaranteed end-to-end response times; an implementation can expose its own supported subset.
Prepare mono WAV audio at the producer processor’s 16 kHz sample rate. When the language is known, use the corresponding locale conditioning; automatic mode detects the spoken language. The basic pipeline example uses a US-English prompt by default, so multilingual behavior should be configured deliberately rather than assumed from that example.
In automatic mode, the producer’s workflow can append a language tag after the transcript’s terminal punctuation. Keeping the tag makes the language available to an application handling mixed-language traffic; stripping it returns clean transcript text. Preserve the distinction between the recognized words, their punctuation and this language metadata when displaying or processing the result.
A captioning application can feed audio in order, collect the transcript and use completed text for its display or downstream processing. A voice application can pass recognized speech onward to its language-understanding stage. Call-analysis work can retain the original recording and transcript together so names, numbers and unfamiliar terms remain inspectable. The ASR model supplies the transcript; summarization, actions and speech generation are separate stages of the application.
NVIDIA trains the model on a blend of public and proprietary speech data with human and synthetic labels normalized to punctuated, properly cased text. Its adaptation guide emphasizes matching language labels and text style, then assessing held-out audio at the streaming setting intended for use. It describes language, accent and domain adaptation without making those customized results properties of the original checkpoint.
The model card ties practical recording length to available memory rather than publishing one universal duration ceiling. Keep the multilingual edition distinct from the English-only model, and retain the selected language and streaming configuration with the transcription result. The output artifact is the recognized text, with optional producer language metadata, rather than a general conversational answer.
", "aboutText": "Nemotron 3.5 ASR Streaming Multilingual 0.6B is NVIDIA’s 600-million-parameter speech-recognition model for live transcription and recorded-audio workloads. It extends the English streaming line with language-ID conditioning. Speech becomes text in the input language, with punctuation and capitalization built into the output.\n\nThe maker’s language coverage has three tiers. Nineteen transcription-ready locales cover US and UK English, US and Spain Spanish, France and Canada French, Italian, Brazil and Portugal Portuguese, Dutch, German, Turkish, Russian, Arabic, Hindi, Japanese, Korean, Vietnamese and Ukrainian. Thirteen broad-coverage locales add Polish, Swedish, Czech, Norwegian Bokmål, Danish, Bulgarian, Finnish, Croatian, Slovak, Mandarin, Hungarian, Romanian and Estonian. Together these 32 locales produce transcription without additional fine-tuning.\n\nEight adaptation-ready locales—Greek, Lithuanian, Latvian, Maltese, Slovenian, Hebrew, Thai and Norwegian Nynorsk—are recognized by the tokenizer but require in-domain fine-tuning to enable full transcription. Thus the producer’s 40-locale figure is not a claim that all forty have the same out-of-the-box capability. A customized checkpoint is a separate artifact from the base model.\n\nThe architecture combines a 24-layer cache-aware FastConformer encoder with an RNN-T decoder. It retains encoder attention and convolution states from earlier frames and processes each new audio chunk without repeatedly recomputing overlapping windows. A language-ID signal is supplied alongside the acoustic representation to condition the transcript or let the model infer the language.\n\nNVIDIA’s NeMo configuration documents chunk sizes of 80, 160, 320, 560 and 1120 milliseconds. The attention setting controls how much future audio a chunk receives before text is emitted, allowing a task to balance waiting time and acoustic context. These are producer streaming configurations, not guaranteed end-to-end response times; an implementation can expose its own supported subset.\n\nPrepare mono WAV audio at the producer processor’s 16 kHz sample rate. When the language is known, use the corresponding locale conditioning; automatic mode detects the spoken language. The basic pipeline example uses a US-English prompt by default, so multilingual behavior should be configured deliberately rather than assumed from that example.\n\nIn automatic mode, the producer’s workflow can append a language tag after the transcript’s terminal punctuation. Keeping the tag makes the language available to an application handling mixed-language traffic; stripping it returns clean transcript text. Preserve the distinction between the recognized words, their punctuation and this language metadata when displaying or processing the result.\n\nA captioning application can feed audio in order, collect the transcript and use completed text for its display or downstream processing. A voice application can pass recognized speech onward to its language-understanding stage. Call-analysis work can retain the original recording and transcript together so names, numbers and unfamiliar terms remain inspectable. The ASR model supplies the transcript; summarization, actions and speech generation are separate stages of the application.\n\nNVIDIA trains the model on a blend of public and proprietary speech data with human and synthetic labels normalized to punctuated, properly cased text. Its adaptation guide emphasizes matching language labels and text style, then assessing held-out audio at the streaming setting intended for use. It describes language, accent and domain adaptation without making those customized results properties of the original checkpoint.\n\nThe model card ties practical recording length to available memory rather than publishing one universal duration ceiling. Keep the multilingual edition distinct from the English-only model, and retain the selected language and streaming configuration with the transcription result. The output artifact is the recognized text, with optional producer language metadata, rather than a general conversational answer.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "267e2fdfd9c9ddb75e4398c82c5d5b4ba889519f296a5bc8e02e3571be3dc97b", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "nvidia/nemotron-3.5-asr-streaming-multilingual-0.6bNVIDIA’s Nemotron 3.5 ASR Streaming Multilingual 0.6B transcribes speech into punctuated text using a cache-aware streaming encoder. It supports explicit language conditioning or automatic detection, with 32 locales ready for transcription and eight requiring adaptation.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Nemotron 3.5 ASR Streaming Multilingual 0.6B is NVIDIA’s 600-million-parameter speech-recognition model for live transcription and recorded-audio workloads. It extends the English streaming line with language-ID conditioning. Speech becomes text in the input language, with punctuation and capitalization built into the output.
The maker’s language coverage has three tiers. Nineteen transcription-ready locales cover US and UK English, US and Spain Spanish, France and Canada French, Italian, Brazil and Portugal Portuguese, Dutch, German, Turkish, Russian, Arabic, Hindi, Japanese, Korean, Vietnamese and Ukrainian. Thirteen broad-coverage locales add Polish, Swedish, Czech, Norwegian Bokmål, Danish, Bulgarian, Finnish, Croatian, Slovak, Mandarin, Hungarian, Romanian and Estonian. Together these 32 locales produce transcription without additional fine-tuning.
Eight adaptation-ready locales—Greek, Lithuanian, Latvian, Maltese, Slovenian, Hebrew, Thai and Norwegian Nynorsk—are recognized by the tokenizer but require in-domain fine-tuning to enable full transcription. Thus the producer’s 40-locale figure is not a claim that all forty have the same out-of-the-box capability. A customized checkpoint is a separate artifact from the base model.
The architecture combines a 24-layer cache-aware FastConformer encoder with an RNN-T decoder. It retains encoder attention and convolution states from earlier frames and processes each new audio chunk without repeatedly recomputing overlapping windows. A language-ID signal is supplied alongside the acoustic representation to condition the transcript or let the model infer the language.
NVIDIA’s NeMo configuration documents chunk sizes of 80, 160, 320, 560 and 1120 milliseconds. The attention setting controls how much future audio a chunk receives before text is emitted, allowing a task to balance waiting time and acoustic context. These are producer streaming configurations, not guaranteed end-to-end response times; an implementation can expose its own supported subset.
Prepare mono WAV audio at the producer processor’s 16 kHz sample rate. When the language is known, use the corresponding locale conditioning; automatic mode detects the spoken language. The basic pipeline example uses a US-English prompt by default, so multilingual behavior should be configured deliberately rather than assumed from that example.
In automatic mode, the producer’s workflow can append a language tag after the transcript’s terminal punctuation. Keeping the tag makes the language available to an application handling mixed-language traffic; stripping it returns clean transcript text. Preserve the distinction between the recognized words, their punctuation and this language metadata when displaying or processing the result.
A captioning application can feed audio in order, collect the transcript and use completed text for its display or downstream processing. A voice application can pass recognized speech onward to its language-understanding stage. Call-analysis work can retain the original recording and transcript together so names, numbers and unfamiliar terms remain inspectable. The ASR model supplies the transcript; summarization, actions and speech generation are separate stages of the application.
NVIDIA trains the model on a blend of public and proprietary speech data with human and synthetic labels normalized to punctuated, properly cased text. Its adaptation guide emphasizes matching language labels and text style, then assessing held-out audio at the streaming setting intended for use. It describes language, accent and domain adaptation without making those customized results properties of the original checkpoint.
The model card ties practical recording length to available memory rather than publishing one universal duration ceiling. Keep the multilingual edition distinct from the English-only model, and retain the selected language and streaming configuration with the transcription result. The output artifact is the recognized text, with optional producer language metadata, rather than a general conversational answer.
POST /v1/audio/transcriptionsaudiotext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits nvidia/nemotron-3.5-asr-streaming-multilingual-0.6b | Default— |
Fieldinput_audio | Typeobject | RequirementRequired | Values & limits | Default— |
Fieldlanguage | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Minimum: 0 Maximum: 1 | Default— |
Fieldresponse_format | Typestring | RequirementOptional | Values & limits json, verbose_json | Defaultjson |
Fieldtimestamp_granularities | Typearray | RequirementOptional | Values & limits | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits | Default— |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, input_audio |
Parametermaterial_types | Contractaudio |
Parametermaterial_conditions | ContractRead an actual WAV file and encode its real bytes for the declared JSON input_audio; no multipart route is advertised for this exact JSON-only declaration. |
Lifecycle: synchronous_result_then_owned_library
GET /v1/media/tasks/{task_id} · actual returned task_id or media library record
GET /v1/media/assets/{asset_id}/content
GET /v1/media/assets
GET /v1/media/assets/{asset_id}
The displayed rates estimate the cost before generation. The final charge is calculated from the cost recorded for the original generation. Measured output duration and forecast quantities are not the final billable quantity.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Forecast quantity | Billing unit | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Forecast quantity— | Billing unit— | Charge Input · Input tokens · Estimate for comparison only — / 1K tokens Output · Output tokens · Estimate for comparison only — / 1K tokens Input · Cache read · Estimate for comparison only — / 1K tokens Input · Cache write · Estimate for comparison only — / 1K tokens Input · Cache write, 1 hour · Estimate for comparison only — / 1K tokens |
| Configurationinput audio second | Rate schedule— | Forecast quantity— | Billing unitper input audio second | Charge Input · input audio second · Estimate for comparison only $0.00000666 per input audio second |
| ConfigurationEstimated generation cost | Rate schedule— | Forecast quantityone generation estimate | Billing unit— | Charge Output · Estimated generation cost · Estimate for comparison only $0.00000666 estimate before generation This estimate is not an additional charge or a fixed rate for the completed generation. |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_AUDIO_FILE:?Set the path to your actual WAV recording}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\nimport base64\naudio=Path(os.environ['AILOOT_AUDIO_FILE']).read_bytes()\nif not (audio[:4]==b'RIFF' and audio[8:12]==b'WAVE'): raise SystemExit('Supply an actual WAV audio file')\nif len(audio)>26214400: raise SystemExit('Use a documented supported large-file flow')\nrequest={'model': 'nvidia/nemotron-3.5-asr-streaming-multilingual-0.6b', 'input_audio': {'data': '${BASE64_ACTUAL_WAV_BYTES}', 'format': 'wav'}}\nrequest['input_audio']['data']=base64.b64encode(audio).decode('ascii')\nPath('request.json').write_text(json.dumps(request,ensure_ascii=False),encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/audio/transcriptions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o response.bin\n# Keep the complete original response and headers. Read every returned segment, word and usage field.\n# Preserve actual operation/task/asset IDs; continue reads of the original result without resubmitting.\nGET /v1/media/tasks/{task_id}GET /v1/media/assets/{asset_id}/contentGET /v1/media/assetsGET /v1/media/assets/{asset_id}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/media/assets \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o owned-assets.json\n# Preserve every returned asset and follow its exact signed content URL. Do not construct a signed token.\nKeep every complete returned response, file and content part.
Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.
Keep the returned X-Client-Request-ID for reconciliation. Read only actual returned task_id/asset_id values or the owned media library; never build a task URL from the operation ID and never resubmit creation.
| Field | Type |
|---|---|
Fieldtext | Type |
Fieldusage | Type |
Fieldsegments[] | Type |
Fieldwords[] | Type |
Fieldspeaker fields | Type |
Fieldtimestamps | Type |
Ailoot Ultra nvidia/nemotron-3.5-asr-streaming-multilingual-0.6b
Use the method paths shown above.