feat: add ASR CTC and seq2seq support

[rylativity]

What does this PR do ?

[EXPERIMENTAL FEATURE] Adds ASR support for Whisper (5 variants) and Parakeet CTC (2 variants) with distributed training and PEFT and lays groundwork for incorporating additional ASR models in Nemo Automodel

Changelog

New Model Support:

• Add NeMoAutoModelForSpeechSeq2Seq for encoder-decoder ASR models (Whisper family: tiny/base/small/medium/large-v3)
• Add NeMoAutoModelForCTC for CTC-based ASR models (Parakeet CTC: 0.6B/1.1B)

New Components:

• Add ASR dataset component with LibriSpeech, Common Voice, and custom dataset loaders (nemo_automodel/components/datasets/asr/)
• Add processor-specific collate functions with automatic mel-spectrogram extraction and tokenization (nemo_automodel/components/datasets/asr/collate_fns.py)
• Implement collate function registry for automatic processor selection

New Recipe:

• Add ASR fine-tuning recipe with support for both CTC and Seq2Seq loss computation (nemo_automodel/recipes/asr/finetune.py)
• Implement validation loop with loss tracking and metrics logging
• Add pipeline parallelism support via AutoPipeline

Example Configurations:

• Add 8 YAML configs for Whisper and Parakeet models with full and PEFT fine-tuning examples
• Add finetune.py entry point script for ASR examples (examples/asr_finetune/finetune.py)
• Include distributed training configurations with device mesh setup

Testing:

• Add 4 functional tests covering Whisper and Parakeet fine-tuning (full and PEFT) (tests/functional_tests/asr_finetune/)
• Add comprehensive unit tests for dataset loaders and collate functions (tests/unit_tests/datasets/asr/)
• Include pytest test class with parameterized model/PEFT configurations

Documentation:

• Add comprehensive README for ASR fine-tuning with quick start examples, PEFT guide, and troubleshooting (examples/asr_finetune/README.md)
• Update root README with ASR examples and usage
• Add inline documentation for ASR model classes and dataset utilities

Dependencies:

• Add librosa and torchcodec as ASR extras in pyproject.toml
• Update Docker build with ASR-specific dependencies

Other:

• Update model exports in nemo_automodel/init.py and _transformers/init.py
• Ensure component independence (no cross-component imports, verified by lint-imports)
• Add copyright year 2026 across new files

Pre checks:

• Make sure you read and followed Contributor guidelines
• Did you write any new necessary tests?
• Did you add or update any necessary documentation?
• Linting/formatting passed
• Commits DCO signed
• Confirmed documentation builds successfully

(Note: Previously opened in PR #1263)

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[akoumpa]

/ok to test 1dad056