# Parakeet Unified EN 0.6B (slim)

> Parakeet Unified EN 0.6B is NVIDIA's open-weight English speech recognition model, a 600M FastConformer encoder with an RNN-T decoder trained for both offline and streaming use down to 160 ms of latency, with punctuation, published as a NeMo archive.

- Full: https://www.anchorterminal.com/tools/parakeet-unified-en-0-6b.md (~7,450 tokens) · this version ~1,730 tokens · JSON https://www.anchorterminal.com/tools/parakeet-unified-en-0-6b.json · canonical https://www.anchorterminal.com/tools/parakeet-unified-en-0-6b
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10 · pricing https://www.anchorterminal.com/tools/parakeet-unified-en-0-6b#pricing · reviews https://www.anchorterminal.com/tools/parakeet-unified-en-0-6b#reviews
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

**D · 47.5/100 · rank #154 of 160 open-weight models · #31 in Speech-to-text · not agent-ready · confidence low**


Assessment: One English model for batch and live transcription, with a well-documented data list and an NVIDIA team that answers. It runs only in NVIDIA NeMo from a pickle-format archive, with no safetensors, GGUF or quantised build, returns plain text without timestamps, and hasn't changed since June.

## Facts

- Kind: Open-weight model · vendor: NVIDIA · category: Speech-to-text · legal entity: NVIDIA Corporation · provenance 74/100
- Packages: huggingface `nvidia/parakeet-unified-en-0.6b`
- Auth: None · pricing: Free · x402: no · licence: NVIDIA Open Model License
- Benchmark probes: not run yet, so Performance is pending
- Surface graded: The open weights at huggingface.co/nvidia/parakeet-unified-en-0.6b, read from the model card, its safety, bias and privacy files, the file list and metadata, the commit history and the runtimes' own code at their latest releases on 10 November 2026. NVIDIA's licence terms and security pages were read for its other listings. Nothing was downloaded or run
- Model: A FastConformer encoder with 24 layers, trained jointly in offline and streaming modes, and an RNN-T decoder, 600M parameters per the card. The repository has no config for the Hub to count parameters from. Audio in, English text out with punctuation and capitalisation
- Languages: English only. The training data is about 250,000 hours of US English, and the card's bias file gives the validation data as 80% American and 10% British English
- Files: One NeMo archive, parakeet-unified-en-0.6b.nemo (2.47 GB, FP32), which holds a PyTorch checkpoint, a pickle format. No safetensors, no GGUF and no config.json. Hugging Face lists a SHA-256 for it
- Licence: The NVIDIA Open Model License, a custom licence that allows commercial use. The card says the model is ready for commercial and non-commercial use
- Runtimes: NVIDIA NeMo only (2.7.3 per the card), through ASRModel.from_pretrained and NeMo's chunked streaming script. Transformers 5.22.2 has Parakeet classes but the repository has no Transformers files, and vLLM, SGLang, llama.cpp, Ollama and LM Studio don't run it
- Input and output: Mono wav audio in. Output is a string of English text with punctuation and capitalisation, read from output[0].text. The card documents no timestamps. Streaming latency is the chunk plus the right context, from 2.08 s down to 0.16 s, with a 5.6 s left context
- Hardware: No memory figure on the card, which says the maximum audio length depends on GPU memory. Tested on NVIDIA V100, A100, A6000 and DGX Spark, on Linux
- Training data: Mostly the English part of NVIDIA's Granary dataset: YouTube-Commons (109,500 hours), YODAS2 (102,000), LibriLight (49,500) and Mosel (14,000), plus LibriSpeech, Fisher, Switchboard, WSJ, the National Speech Corpus, VCTK, VoxPopuli, Europarl-ASR, Multilingual LibriSpeech, Common Voice, People's Speech and AMI. About 250,000 hours in all, with some transcripts written by people and some by ASR models

- Scores: Capability pending, Reach 50, Risk 35, Agent interface 35, Documentation and upkeep 79 · total over the 7 assessed categories
- Why:  · Reach, Memory 30, the only build is the FP32 NeMo archive, 2.47 GB, and 2.47 GB × 1.2 = 3.0 GB, under 16 GB. · Risk, File safety 0, the only weights file is parakeet-unified-en-0.6b.nemo, a NeMo archive whose weights are a PyTorch checkpoint read with torch… · Agent interface, Chat template 0, no chat template and no prompt. · Documentation and upkeep, Model card 14, Intended use, limitations (buffered streaming only, accuracy falling at 160 ms and below), architecture, parameters (600M) an…
- Sources: 13, open questions: 5, both in the full twin
- Capabilities: speech.stt, speech.batch, speech.streaming, model.open-weights
- JSON: https://www.anchorterminal.com/api/v1/tools/parakeet-unified-en-0-6b.json
- Its vendor verifies the listing with `verify_listing` or `POST https://www.anchorterminal.com/api/v1/verify` (the full twin says how); no effect on the grade.
- Support: https://github.com/NVIDIA-NeMo/Speech/issues; https://huggingface.co/nvidia/parakeet-unified-en-0.6b/discussions; more in the full twin

## Before you call it

1. Load it with NeMo: `nemo_asr.models.ASRModel.from_pretrained(model_name='nvidia/parakeet-unified-en-0.6b')`, then `transcribe([path])[0].text`
2. Loading a .nemo archive unpickles a PyTorch checkpoint, so only load it from NVIDIA's repository
3. Send mono wav audio. English only
4. For live audio use NeMo's chunked RNN-T streaming script with a 5.6 s left context, and pick the chunk and right context from the card's table
5. Below 240 ms of latency accuracy drops, and at 80 ms NVIDIA recommends its nemotron-speech-streaming-en-0.6b instead
6. If you need word timestamps, use Parakeet TDT 0.6B v3

## Connect

```bash
pip install -U "nemo_toolkit[asr]"
python -c "import nemo.collections.asr as nemo_asr; m = nemo_asr.models.ASRModel.from_pretrained(model_name='nvidia/parakeet-unified-en-0.6b'); print(m.transcribe(['speech.wav'])[0].text)"
```

```bash
# no HTTP server ships with it. For streaming, NeMo's script:
python examples/asr/asr_chunked_inference/rnnt/speech_to_text_streaming_infer_rnnt.py \
  pretrained_name=nvidia/parakeet-unified-en-0.6b dataset_manifest=manifest.json \
  left_context_secs=5.6 chunk_secs=0.56 right_context_secs=0.56 att_context_size_as_chunk=true
```

## Similar tools

| Tool | Grade | Score | Shared capabilities | Slim |
| --- | --- | --- | --- | --- |
| Parakeet TDT 0.6B v3 | B | 66.9 | speech.stt, speech.batch, speech.streaming, model.open-weights | https://www.anchorterminal.com/tools/parakeet-tdt-0-6b-v3.min.md |
| Qwen3-ASR-1.7B | B | 64.4 | speech.stt, speech.batch, speech.streaming, model.open-weights | https://www.anchorterminal.com/tools/qwen3-asr-1-7b.min.md |
| Qwen3-ASR-0.6B | C | 61.2 | speech.stt, speech.batch, speech.streaming, model.open-weights | https://www.anchorterminal.com/tools/qwen3-asr-0-6b.min.md |
| Nemotron 3.5 ASR Streaming 0.6B | C | 60.3 | speech.stt, speech.streaming, speech.batch, model.open-weights | https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.min.md |
| Azure AI Speech speech-to-text | BB | 75.1 | speech.stt, speech.streaming, speech.batch | https://www.anchorterminal.com/tools/azure-speech-to-text.min.md |


## Panel reviews (0, desk reviews from public material, no calls made)