MODEL PROFILE

parakeet-tdt-0.6b-v3

litert-community · parakeet-tdt-0.6b-v3

This is nvidia/parakeet-tdt-0.6b-v3 (600M-parameter FastConformer encoder + Token-and-Duration Transducer, TDT, decoder) packaged as a `.litertlm` bundle for LiteRT-LM, Google's open-source on-device inference runtime, alongside standalone `.tflite` and AOT-compiled NPU variants for LiteRT. Everything needed to transcribe arbitrary-length audio with automatic language detection, punctuation, and capitalization is inside the one `.litertlm` file: INT8 encoder, hybrid stateful/stateless TDT decoder, unified 8,192-token multilingual SentencePiece tokenizer, and `Speech2TextMetadata` (16 kHz resampler and 128-bin Slaney log-mel front-end configuration) — no Python and no network required. All commands below accept mono or multi-channel WAV files at any sample rate (automatically resampled to 16 kHz mono) and stream confirmed and unconfirmed transcript segments to stdout (or `--output_path`). Prerequisites (Bazel, Android NDK) are in the LiteRT-LM build guide. > [!TIP] > If you omit `--model_path` and pass `--model_name=parakeet-tdt-0.6b-v3`, > `omni_runner` automatically downloads > `parakeet_tdt_0.6b_v3_5s_i8_stateful.litertlm` into `--cache_dir` on first > run.

Speech recognitionlicense:cc-by-4.0
Context windowNot disclosed
Input → OutputAudio → Text
Model weightsNot verified
Profile updated2026/11/2 19:15:48
OVERVIEW

Model overview

This is nvidia/parakeet-tdt-0.6b-v3 (600M-parameter FastConformer encoder + Token-and-Duration Transducer, TDT, decoder) packaged as a `.litertlm` bundle for LiteRT-LM, Google's open-source on-device inference runtime, alongside standalone `.tflite` and AOT-compiled NPU variants for LiteRT. Everything needed to transcribe arbitrary-length audio with automatic language detection, punctuation, and capitalization is inside the one `.litertlm` file: INT8 encoder, hybrid stateful/stateless TDT decoder, unified 8,192-token multilingual SentencePiece tokenizer, and `Speech2TextMetadata` (16 kHz resampler and 128-bin Slaney log-mel front-end configuration) — no Python and no network required. All commands below accept mono or multi-channel WAV files at any sample rate (automatically resampled to 16 kHz mono) and stream confirmed and unconfirmed transcript segments to stdout (or `--output_path`). Prerequisites (Bazel, Android NDK) are in the LiteRT-LM build guide. > [!TIP] > If you omit `--model_path` and pass `--model_name=parakeet-tdt-0.6b-v3`, > `omni_runner` automatically downloads > `parakeet_tdt_0.6b_v3_5s_i8_stateful.litertlm` into `--cache_dir` on first > run.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

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Sources & traceability

许可证:cc-by-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。