SpeechLM2 ================================ .. note:: The SpeechLM2 collection is still in active development and the code is likely to keep changing. SpeechLM2 refers to a collection that augments pre-trained Large Language Models (LLMs) with speech understanding and generation capabilities. This collection is designed to be compact, efficient, and to support easy swapping of different LLMs backed by HuggingFace AutoModel. It has a first-class support for using dynamic batch sizes via Lhotse and various model parallelism techniques (e.g., FSDP2, Tensor Parallel, Sequence Parallel) via PyTorch DTensor API. We currently support six main model types: * **SALM** (Speech-Augmented Language Model) - a simple but effective approach to augmenting pre-trained LLMs with speech understanding capabilities. * **DuplexS2SModel** - a full-duplex speech-to-speech model with an ASR encoder, directly predicting discrete audio codes. * **DuplexS2SSpeechDecoderModel** - a variant of DuplexS2SModel with a separate transformer decoder for speech generation. * **DuplexEARTTS** - a ready-to-use duplex text-to-speech model that supports user interruption via a special text interruption token. * **DuplexSTTModel** - a decoder model to generate agent text in duplex, in response to both user speech and text inputs. * **NemotronVoiceChat** - an *inference-only* pipeline that seamlessly merges `DuplexSTTModel` and `DuplexEARTTS` to deliver an end-to-end, full-duplex conversational agent with high-fidelity speech generation. Using Pretrained Models ----------------------- After :ref:`installing NeMo`, you can load and use a pretrained speechlm2 model as follows: .. code-block:: python import nemo.collections.speechlm2 as slm # Load a pretrained SALM model model = slm.models.SALM.from_pretrained("model_name_or_path") # Set model to evaluation mode model = model.eval() Inference with Pretrained Models -------------------------------- SALM **** You can run inference using the loaded pretrained SALM model: .. code-block:: python import torch import soundfile as sf from nemo.collections.audio.parts.utils.transforms import resample import nemo.collections.speechlm2 as slm model = slm.models.SALM.from_pretrained("path/to/pretrained_checkpoint").eval() # Load audio file audio_path = "path/to/audio.wav" audio_signal, sample_rate = sf.read(audio_path) audio_signal = torch.tensor(audio_signal).unsqueeze(0) # Resample if needed if sample_rate != 16000: # Most models expect 16kHz audio audio_signal = resample(audio_signal, sample_rate, 16000) sample_rate = 16000 # Prepare audio for model audio_signal = audio_signal.to(model.device) audio_len = torch.tensor([audio_signal.shape[1]], device=model.device) # Create a prompt for SALM model inference # The audio_locator_tag is a special token that will be replaced with audio embeddings prompt = [{"role": "user", "content": f"{model.audio_locator_tag}"}] # Generate response with torch.no_grad(): output = model.generate( prompts=[prompt], audios=audio_signal, audio_lens=audio_len, generation_config=None # You can customize generation parameters here ) # Process the output tokens response = model.tokenizer.ids_to_text(output[0]) print(f"Model response: {response}") DuplexS2SModel ************** You can run inference using the loaded pretrained DuplexS2SModel: .. code-block:: python import torch import soundfile as sf from nemo.collections.audio.parts.utils.transforms import resample import nemo.collections.speechlm2 as slm model = slm.models.DuplexS2SModel.from_pretrained("path/to/pretrained_checkpoint").eval() # Load audio file audio_path = "path/to/audio.wav" audio_signal, sample_rate = sf.read(audio_path) audio_signal = torch.tensor(audio_signal).unsqueeze(0) # Resample if needed if sample_rate != 16000: # Most models expect 16kHz audio audio_signal = resample(audio_signal, sample_rate, 16000) sample_rate = 16000 # Prepare audio for model audio_signal = audio_signal.to(model.device) audio_len = torch.tensor([audio_signal.shape[1]], device=model.device) # Run offline inference results = model.offline_inference( input_signal=audio_signal, input_signal_lens=audio_len ) # Decode text and audio tokens transcription = results["text"][0] audio = results["audio"][0] DuplexSTTModel ************** You can run inference using the loaded pretrained DuplexSTTModel: .. code-block:: python import torch import soundfile as sf from nemo.collections.audio.parts.utils.transforms import resample import nemo.collections.speechlm2 as slm model = slm.models.DuplexSTTModel.from_pretrained("path/to/pretrained_checkpoint").eval() # Load audio file audio_path = "path/to/audio.wav" audio_signal, sample_rate = sf.read(audio_path) audio_signal = torch.tensor(audio_signal).unsqueeze(0) # Resample if needed if sample_rate != 16000: audio_signal = resample(audio_signal, sample_rate, 16000) sample_rate = 16000 # Prepare audio for model audio_signal = audio_signal.to(model.device) audio_len = torch.tensor([audio_signal.shape[1]], device=model.device) # Run offline inference - generates text output only results = model.offline_inference( input_signal=audio_signal, input_signal_lens=audio_len ) # Decode text tokens transcription = results["text"][0] print(f"Transcription: {transcription}") DuplexEARTTS ************ Because `DuplexEARTTS` relies on precise token padding and EOS placement to handle potential user interruptions, inference and evaluation are handled via the `duplex_eartts_eval.py` script following the MagpieTTS dataset format recipe. The evaluation script processes a `JSONL` file where each line is a dictionary containing the text, the reference audio for the speaker, and the desired output audio filename. **JSONL Format Examples:** Single-Turn format (evaluates a continuous string): .. code-block:: json {"text": "Like really quickly and then they run off.", "context_audio_filepath": "speaker_1.wav", "audio_filepath": "audio_1.wav"} Multi-Turn format (evaluates sequential conversational turns, padded incrementally): .. code-block:: json {"text": ["Yes.", "Sure.", "Right.", "I get what you’re saying."], "context_audio_filepath": "speaker_2.wav", "audio_filepath": "audio_2.wav"} **Running the Evaluation/Inference Script:** .. code-block:: bash python examples/speechlm2/duplex_eartts_eval.py \ --config-path=conf/ \ --config-name=duplex_eartts.yaml \ ++checkpoint_path=/path/to/duplex_eartts/model.ckpt \ ++datasets_json_path=/path/to/evalset_config.jsonl \ ++out_dir=/path/to/output/audio_samples/ \ ++user_custom_speaker_reference=/path/to/optional_override_speaker.wav The script will decode the text, apply the target speaker conditioning, generate the resulting audio waveforms into `out_dir`, and compute ASR intelligibility metrics (CER/WER) on the generated speech. NemotronVoiceChat ***************** You can evaluate and run full-duplex inference using the `NemotronVoiceChat` pipeline. This model natively chains the `DuplexSTTModel` with the `DuplexEARTTS` speech decoder for an end-to-end response: .. code-block:: python import torch import soundfile as sf from nemo.collections.audio.parts.utils.transforms import resample import nemo.collections.speechlm2 as slm model = slm.models.NemotronVoiceChat.from_pretrained("path/to/pretrained_checkpoint").eval() # Load user audio prompt audio_path = "path/to/user_audio.wav" audio_signal, sample_rate = sf.read(audio_path) audio_signal = torch.tensor(audio_signal).unsqueeze(0) # Resample to the source_sample_rate (usually 16kHz for STT perception) if sample_rate != 16000: audio_signal = resample(audio_signal, sample_rate, 16000) sample_rate = 16000 # Prepare audio for model audio_signal = audio_signal.to(model.device) audio_len = torch.tensor([audio_signal.shape[1]], device=model.device) # (Optional) Load an explicit speaker reference audio to condition the agent's voice # speaker_audio, _ = sf.read("path/to/speaker_reference.wav") # speaker_audio = torch.tensor(speaker_audio).unsqueeze(0).to(model.device) # speaker_len = torch.tensor([speaker_audio.shape[1]], device=model.device) # Note: If an explicit audio reference is not passed into `offline_inference`, # the model relies on the internal config parameters: # 1. model.cfg.inference_speaker_name (Highest priority preset, e.g., 'Megan') # 2. model.cfg.inference_speaker_reference (Fallback audio file path) # Run full offline inference results = model.offline_inference( input_signal=audio_signal, input_signal_lens=audio_len, # speaker_audio=speaker_audio, # Pass speaker reference if available # speaker_audio_lens=speaker_len ) # Decode the predicted text and generated speech waveform generated_text = results["text"][0] generated_speech = results["audio"][0] print(f"Agent response: {generated_text}") # generated_speech can now be saved or played (sampled at model.target_sample_rate) Training a Model ---------------- This example demonstrates how to train a SALM model. .. note:: **NemotronVoiceChat is an inference-only class.** It does not implement a `training_step` and cannot be trained using the pipeline below. To update its underlying capabilities, you must train the `DuplexSTTModel` and `DuplexEARTTS` models independently. .. code-block:: python from omegaconf import OmegaConf import torch from lightning.pytorch import Trainer from lightning.pytorch.strategies import ModelParallelStrategy import nemo.collections.speechlm2 as slm from nemo.collections.speechlm2.data import SALMDataset, DataModule from nemo.utils.exp_manager import exp_manager # Load configuration config_path = "path/to/config.yaml" # E.g., from examples/speechlm2/conf/salm.yaml cfg = OmegaConf.load(config_path) # Initialize PyTorch Lightning trainer trainer = Trainer( max_steps=100000, accelerator="gpu", devices=1, precision="bf16-true", strategy=ModelParallelStrategy(data_parallel_size=2, tensor_parallel_size=1), limit_train_batches=1000, val_check_interval=1000, use_distributed_sampler=False, logger=False, enable_checkpointing=False, ) # Set up experiment manager for logging exp_manager(trainer, cfg.get("exp_manager", None)) # Initialize model with configuration model = slm.models.SALM(OmegaConf.to_container(cfg.model, resolve=True)) # Create dataset and datamodule dataset = SALMDataset(tokenizer=model.tokenizer) datamodule = DataModule(cfg.data, tokenizer=model.tokenizer, dataset=dataset) # Train the model trainer.fit(model, datamodule) Example Using Command-Line Training Script ------------------------------------------ Alternatively, you can train a model using the provided training scripts in the examples directory: .. code-block:: bash # Train a SALM model python examples/speechlm2/salm_train.py \ --config-path=examples/speechlm2/conf \ --config-name=salm # For SALM inference/evaluation python examples/speechlm2/salm_eval.py \ pretrained_name=/path/to/checkpoint \ inputs=/path/to/test_manifest \ batch_size=64 \ max_new_tokens=128 \ output_manifest=generations.jsonl For more detailed information on training at scale, model parallelism, and SLURM-based training, see :doc:`training and scaling `. Collection Structure -------------------- The speechlm2 collection is organized into the following key components: - **Models**: Contains implementations of DuplexS2SModel, DuplexS2SSpeechDecoderModel, DuplexSTTModel, SALM, DuplexEARTTS, and the inference-only NemotronVoiceChat. - **Modules**: Contains audio perception and speech generation modules. - **Data**: Includes dataset classes and data loading utilities. SpeechLM2 Documentation ----------------------- For more information, see additional sections in the SpeechLM2 docs: .. toctree:: :maxdepth: 1 models datasets configs training_and_scaling