| .. _ten-minutes: |
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| NeMo Speech Inference in 5 Minutes |
| =================================== |
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| This guide gives you a quick, hands-on tour of NeMo's core speech capabilities. By the end, you'll have transcribed audio, synthesized speech, identified speakers, and used a speech language model — all in about 50 lines of code. |
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| .. note:: |
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| Make sure you have :doc:`installed NeMo <install>` before starting. |
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| 1. Transcribe Speech (ASR) |
| -------------------------- |
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| Automatic Speech Recognition converts audio to text. NeMo's Parakeet model sits at the top of the `HuggingFace OpenASR Leaderboard <https://huggingface.co/spaces/hf-audio/open_asr_leaderboard>`_. |
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| **Basic transcription** — 3 lines of code: |
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| .. code-block:: python |
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| import nemo.collections.asr as nemo_asr |
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| asr_model = nemo_asr.models.ASRModel.from_pretrained("nvidia/parakeet-tdt-0.6b-v2") |
| transcript = asr_model.transcribe(["audio.wav"])[0].text |
| print(transcript) |
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| **With timestamps** — know *when* each word was spoken: |
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| .. code-block:: python |
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| hypotheses = asr_model.transcribe(["audio.wav"], timestamps=True) |
| for stamp in hypotheses[0].timestamp['word']: |
| print(f"{stamp['start']}s - {stamp['end']}s : {stamp['word']}") |
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| **From the command line**: |
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| .. code-block:: bash |
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| python examples/asr/transcribe_speech.py \ |
| pretrained_name="nvidia/parakeet-tdt-0.6b-v2" \ |
| audio_dir=./my_audio_files/ |
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| 2. Synthesize Speech (TTS) |
| -------------------------- |
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| Text-to-Speech generates natural audio from text. NeMo's **Magpie TTS** is a multilingual, codec-based model that supports multiple speakers and languages: |
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| .. code-block:: python |
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| from nemo.collections.tts.models import MagpieTTSModel |
| import soundfile as sf |
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| # Load model (multilingual 357M, from Hugging Face) |
| model = MagpieTTSModel.from_pretrained("nvidia/magpie_tts_multilingual_357m") |
| model.eval() |
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| # Generate speech |
| audio, audio_len = model.do_tts( |
| transcript="Hello! Welcome to NeMo speech AI.", |
| language="en", |
| ) |
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| # Save to file |
| sf.write("output.wav", audio[0].cpu().numpy(), 22050) |
| print("Speech saved to output.wav") |
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| 3. Identify Speakers (Diarization) |
| ---------------------------------- |
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| Speaker diarization answers "who spoke when?" in multi-speaker audio. |
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| .. code-block:: python |
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| from nemo.collections.asr.models import SortformerEncLabelModel |
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| diar_model = SortformerEncLabelModel.from_pretrained("nvidia/diar_streaming_sortformer_4spk-v2") |
| diar_model.eval() |
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| segments = diar_model.diarize(audio=["meeting.wav"], batch_size=1) |
| for seg in segments[0]: |
| print(seg) # (begin_seconds, end_seconds, speaker_index) |
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| 4. Speech Language Models (SpeechLM2) |
| ------------------------------------- |
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| SpeechLM2 augments large language models with speech understanding. Canary-Qwen combines an ASR encoder with a Qwen LLM: |
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| .. code-block:: python |
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| from nemo.collections.speechlm2.models import SALM |
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| model = SALM.from_pretrained('nvidia/canary-qwen-2.5b') |
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| answer_ids = model.generate( |
| prompts=[[{ |
| "role": "user", |
| "content": f"Transcribe the following: {model.audio_locator_tag}", |
| "audio": ["speech.wav"], |
| }]], |
| max_new_tokens=128, |
| ) |
| print(model.tokenizer.ids_to_text(answer_ids[0].cpu())) |
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| What's Next? |
| ------------ |
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| Now that you've seen the basics, dive deeper: |
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| - :doc:`key_concepts` — Understand the speech AI fundamentals behind these models |
| - :doc:`choosing_a_model` — Find the best model for your specific use case |
| - :doc:`../asr/intro` — Full ASR documentation |
| - :doc:`../tts/intro` — Full TTS documentation |
| - :doc:`../asr/speaker_diarization/intro` — Speaker diarization and recognition |
| - :doc:`../starthere/tutorials` — Tutorial notebooks |
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