Add unified prompt model support for multilingual ASR and cache aware inference

[enas-albasiri]

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What does this PR do ?

Add unified prompt architecture for multilingual ASR, enabling language-ID conditioned for both Hybrid RNNT-CTC and RNNT models as well as streaming inference support.

Collection: ASR

Changelog

• Add EncDecRNNTBPEModelWithPrompt — RNNT prompt-conditioned model with training script and 600M streaming config
• Add LhotseSpeechToTextBpeDatasetWithPromptIndex — index-based Lhotse dataset with per-dataset prompt mode support (langID/auto/unified) via lhotse input_cfg tags
• Add backward-compatible forward() in hybrid model accepting both legacy prompt tensors [B, T, D] and new prompt indices [B]
• Add set_inference_prompt() + conformer_stream_step() override for streaming inference in both model variants
• Add target_lang parameter to speech_to_text_cache_aware_streaming_infer.py (defaults to "auto" for prompt models)
• Add strip_lang_tags config in RNNTDecodingConfig to remove <xx-XX> locale tags from decoded output

Usage

# Training with per-dataset prompt modes via lhotse input_cfg:
# In your input_cfg yaml, tag datasets with prompt_mode:
#   - type: nemo_tarred
#     tags: { prompt_mode: langID }    # AST data — always pass lang ID
#   - type: nemo_tarred
#     tags: { prompt_mode: unified }   # ASR data — 50/50 ratio
#   - type: nemo_tarred
#     tags: { prompt_mode: auto }      # Code-switching — always auto

# Streaming inference with target language:
# python speech_to_text_cache_aware_streaming_infer.py \
#     model_path=model.nemo \
#     dataset_manifest=test.json \
#     target_lang=en-US

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