Implement Multiturn support for EasyMagpie

[Edresson]

What does this PR do ?

Implements multiturn support for EasyMagpieTTS, including multiturn Lhotse training/evaluation data handling, user-audio-conditioned inference, multiturn metrics/export support, and related tests.

Collection: TTS

Changelog

• Added a new EasyMagpie multiturn Lhotse config: examples/tts/conf/magpietts/easy_magpietts_lhotse_multiturn.yaml.
• Added support for restoring EasyMagpie models from a pretrained checkpoint before normal initialization.
• Added multiturn Lhotse TTS dataset support for duplex / multi-turn conversational samples.
• Added EasyMagpie multiturn user-audio inference mode via --easy_magpie_inference_mode multiturn_user_audio.
• Added support for user-audio conditioning, user silence handling, user turn end-token handling, and max evaluation turns during multiturn inference.
• Added distributed / multi-GPU inference support for multiturn EasyMagpie evaluation by sharding generation across ranks and merging rank-local outputs for evaluation.
• Added grouped turn-level and sample-level metrics export for multiturn evaluation, including JSON/CSV outputs.
• Added phoneme prediction export fields to generated .json / .csv evaluation outputs.
• Added optional predicted-phoneme inference controls, including phoneme input type, phoneme sampling method, tokenizer override, and text-input dropout.
• Added additional evaluation options for ASR / EOU batch sizes, emotion cosine similarity, emotion match rate, and text-annotation stripping for metrics.
• Added Lhotse dataloader speaker filtering support.
• Added / updated Nemotron-H decoder support needed for prefill in EasyMagpie multiturn inference.
• Added unit tests for Lhotse TTS filters, MagpieTTS Lhotse dataset behavior, and EasyMagpieTTS model behavior.

Usage

Example EasyMagpie multiturn inference with a .nemo checkpoint:

# Run from the NeMo repository root.
# Replace paths with your local model, codec, evalset config, and output directory.

python examples/tts/magpietts_inference.py \
  --model_type easy_magpie \
  --easy_magpie_inference_mode multiturn_user_audio \
  --nemo_files /path/to/easy_magpie_model.nemo \
  --datasets_json_path /path/to/evalset_config.json \
  --out_dir /path/to/output \
  --codecmodel_path /path/to/codec_model.nemo \
  --batch_size 1 \
  --max_eval_turns 6 \
  --run_evaluation

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[Edresson]

FYI, these changes was done to support prefill in one step on nemotron_h class.

[Edresson]

/ok to test da88283

[shehzeen]

/ok to test 4156fc2