| .. _choosing-a-model: |
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| Choosing a Model |
| ================ |
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| NeMo offers many pretrained speech models. This guide helps you pick the right one for your use case. |
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| ASR: Which Model Should I Use? |
| ------------------------------ |
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| .. list-table:: |
| :widths: 30 25 45 |
| :header-rows: 1 |
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| * - I want to... |
| - Recommended Model |
| - Why |
| * - Get the best accuracy on English |
| - `Canary-Qwen 2.5B <https://huggingface.co/nvidia/canary-qwen-2.5b>`_ |
| - State-of-the-art English ASR. For very fast offline alternatives with almost SOTA accuracy, use `Parakeet-TDT V2 <https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2>`_ or `Parakeet-TDT V3 <https://huggingface.co/nvidia/parakeet-tdt-1.1b>`_. |
| * - Transcribe multiple languages |
| - `Canary-1B V2 <https://huggingface.co/nvidia/canary-1b-v2>`_ |
| - Supports 25 EU languages + translation between them. AED decoder. |
| * - Transcribe European languages (ASR only, auto language detection) |
| - `Parakeet-TDT 0.6B V3 <https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3>`_ |
| - 25 European languages in one model; automatic language detection; punctuation, capitalization, and word/segment timestamps; long-form and streaming options. No speech-to-text translation—use Canary-1B V2 if you need translation. |
| * - Stream audio in real-time |
| - `Nemotron-Speech-Streaming <https://huggingface.co/nvidia/nemotron-speech-streaming-en-0.6b>`_ |
| - Low-latency streaming English ASR with configurable chunk sizes. Cache-aware FastConformer + RNN-T. |
| * - Minimize model size |
| - `Canary-180M Flash <https://huggingface.co/nvidia/canary-180m-flash>`_ |
| - Smallest multilingual model. Good for edge deployment. |
| * - Use CTC decoding (simpler pipeline) |
| - `Parakeet-CTC-1.1B <https://huggingface.co/nvidia/parakeet-ctc-1.1b>`_ |
| - Non-autoregressive. Fast. Good with external language models. |
| * - Integrate with an external LM |
| - Any Parakeet model + NGPU-LM |
| - GPU-accelerated n-gram LM fusion for CTC, RNNT, and TDT models. |
| * - Transcribe multi-speaker meetings |
| - `Multitalker Parakeet Streaming <https://huggingface.co/nvidia/multitalker-parakeet-streaming-0.6b-v1>`_ |
| - Handles overlapping speech in real-time with speaker-adapted decoding. |
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| TTS: Which Model Should I Use? |
| ------------------------------ |
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| .. list-table:: |
| :widths: 30 25 45 |
| :header-rows: 1 |
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| * - I want to... |
| - Recommended Model |
| - Why |
| * - Generate high-quality multilingual speech |
| - `MagpieTTS <https://huggingface.co/nvidia/magpie_tts_multilingual_357m>`_ |
| - End-to-end LLM-based TTS. Supports voice cloning and multiple languages. |
| * - Fast, controllable English synthesis |
| - `FastPitch <https://huggingface.co/nvidia/tts_en_fastpitch>`_ + `HiFi-GAN <https://huggingface.co/nvidia/tts_hifigan>`_ |
| - Cascaded pipeline with pitch/duration control. Well-tested. |
| * - Generate discrete audio tokens |
| - Audio Codec |
| - Neural audio codec for tokenizing audio. Used by MagpieTTS internally. |
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| Speaker Tasks: Which Model Should I Use? |
| ----------------------------------------- |
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| .. list-table:: |
| :widths: 30 25 45 |
| :header-rows: 1 |
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| * - I want to... |
| - Recommended Model |
| - Why |
| * - Determine who spoke when |
| - `Streaming Sortformer <https://huggingface.co/nvidia/diar_streaming_sortformer_4spk-v2.1>`_, `Offline Sortformer <https://huggingface.co/nvidia/diar_sortformer_4spk-v1>`_ |
| - End-to-end diarization for up to 4 speakers. Use streaming for real-time; use offline for batch. |
| * - Verify/identify a speaker |
| - `TitaNet <https://huggingface.co/nvidia/speakerverification_en_titanet_large>`_ |
| - Extracts speaker embeddings for verification and identification. |
| * - Detect voice activity |
| - `MarbleNet <https://huggingface.co/nvidia/Frame_VAD_Multilingual_MarbleNet_v2.0>`_ |
| - Frame-level VAD. Multilingual. Works as a preprocessing step. |
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| Speech Language Models: Which Model Should I Use? |
| ------------------------------------------------- |
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| .. list-table:: |
| :widths: 30 25 45 |
| :header-rows: 1 |
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| * - I want to... |
| - Recommended Model |
| - Why |
| * - Ask questions about audio content |
| - `Canary-Qwen 2.5B <https://huggingface.co/nvidia/canary-qwen-2.5b>`_ (SALM) |
| - LLM augmented with speech understanding. Can transcribe, translate, and answer questions about audio. |
| * - Build a speech-to-speech system |
| - DuplexS2SModel |
| - Full-duplex model that both understands and generates speech. |
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| Decision Flowchart |
| ------------------ |
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| .. code-block:: text |
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| What do you want to do? |
| │ |
| ├─ Transcribe speech to text (ASR) |
| │ ├─ Best accuracy on English? → Canary-Qwen 2.5B (or Parakeet-TDT V2/V3 for fast offline) |
| │ ├─ Multiple languages + translation? → Canary-1B V2 |
| │ ├─ European multilingual ASR (auto LID)? → Parakeet-TDT 0.6B V3 |
| │ ├─ Stream audio in real-time? → Nemotron-Speech-Streaming |
| │ └─ Multi-speaker meeting? → Multitalker Parakeet Streaming |
| │ |
| ├─ Generate speech from text (TTS) |
| │ ├─ Multilingual / voice cloning? → MagpieTTS |
| │ └─ English with pitch control? → FastPitch + HiFi-GAN |
| │ |
| ├─ Identify speakers |
| │ ├─ Who spoke when? → Streaming Sortformer or Offline Sortformer |
| │ └─ Verify identity? → TitaNet |
| │ |
| ├─ Enhance audio quality → See Audio Processing models |
| │ |
| └─ Speech-aware LLM → Canary-Qwen 2.5B (SALM) |
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| Where to Find Models |
| -------------------- |
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| All pretrained NeMo models are available on: |
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| - `HuggingFace Hub (nvidia) <https://huggingface.co/nvidia>`_ — search for "nemo" or specific model names |
| - `NGC Model Catalog <https://catalog.ngc.nvidia.com/models?query=nemo&orderBy=weightPopularDESC>`_ — NVIDIA's model registry |
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| See :doc:`../checkpoints/intro` for instructions on loading pretrained models. |
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