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NeMo Speech Inference in 5 Minutes
===================================
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.
.. note::
Make sure you have :doc:`installed NeMo <install>` before starting.
1. Transcribe Speech (ASR)
--------------------------
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>`_.
**Basic transcription** — 3 lines of code:
.. code-block:: python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained("nvidia/parakeet-tdt-0.6b-v2")
transcript = asr_model.transcribe(["audio.wav"])[0].text
print(transcript)
**With timestamps** — know *when* each word was spoken:
.. code-block:: python
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']}")
**From the command line**:
.. code-block:: bash
python examples/asr/transcribe_speech.py \
pretrained_name="nvidia/parakeet-tdt-0.6b-v2" \
audio_dir=./my_audio_files/
2. Synthesize Speech (TTS)
--------------------------
Text-to-Speech generates natural audio from text. NeMo's **Magpie TTS** is a multilingual, codec-based model that supports multiple speakers and languages:
.. code-block:: python
from nemo.collections.tts.models import MagpieTTSModel
import soundfile as sf
# Load model (multilingual 357M, from Hugging Face)
model = MagpieTTSModel.from_pretrained("nvidia/magpie_tts_multilingual_357m")
model.eval()
# Generate speech
audio, audio_len = model.do_tts(
transcript="Hello! Welcome to NeMo speech AI.",
language="en",
)
# Save to file
sf.write("output.wav", audio[0].cpu().numpy(), 22050)
print("Speech saved to output.wav")
3. Identify Speakers (Diarization)
----------------------------------
Speaker diarization answers "who spoke when?" in multi-speaker audio.
.. code-block:: python
from nemo.collections.asr.models import SortformerEncLabelModel
diar_model = SortformerEncLabelModel.from_pretrained("nvidia/diar_streaming_sortformer_4spk-v2")
diar_model.eval()
segments = diar_model.diarize(audio=["meeting.wav"], batch_size=1)
for seg in segments[0]:
print(seg) # (begin_seconds, end_seconds, speaker_index)
4. Speech Language Models (SpeechLM2)
-------------------------------------
SpeechLM2 augments large language models with speech understanding. Canary-Qwen combines an ASR encoder with a Qwen LLM:
.. code-block:: python
from nemo.collections.speechlm2.models import SALM
model = SALM.from_pretrained('nvidia/canary-qwen-2.5b')
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()))
What's Next?
------------
Now that you've seen the basics, dive deeper:
- :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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