Update private_README.md and add figures (PNGs via LFS)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

.gitattributes

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 *.zst filter=lfs diff=lfs merge=lfs -text
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+*.png filter=lfs diff=lfs merge=lfs -text

private_README.md

@@ -1,12 +1,177 @@
 ---
 license: other
-license_name: nvidia-software-and-model-evaluation-license
+license_name: nvidia-open-model-license
 license_link: >-
-  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-and-model-evaluation-license/
+  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+library_name: nemo
+language:
+- en
+- es
+- de
+- fr
+- it
+- ar
+- ja
+- ko
+- pt
+- ru
+- hi
+- zh
+- vi
+- he
+- nl
+- cs
+- da
+- pl
+- 'no'
+- sv
+- th
+- tr
+- bg
+- el
+- et
+- fi
+- hr
+- hu
+- lt
+- lv
+- ro
+- sk
+- uk
+- mt
+- sl
+datasets:
+- nvidia/Granary
+- multilingual_librispeech
+- fleurs
+- mozilla-foundation/common_voice_8_0
+- voxpopuli
+- europarl
+thumbnail: null
+tags:
+- speech-recognition
+- cache-aware ASR
+- automatic-speech-recognition
+- streaming-asr
+- multilingual
+- speech
+- audio
+- FastConformer
+- RNNT
+- Parakeet
+- ASR
+- pytorch
+- NeMo
+widget:
+- example_title: Librispeech sample 1
+  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
+- example_title: Librispeech sample 2
+  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
+model-index:
+- name: nemotron-asr-streaming-multilingual-0.6b
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: FLEURS (English)
+      type: google/fleurs
+      config: en_us
+      split: test
+    metrics:
+    - name: WER (1.12s frame size, LangID)
+      type: wer
+      value: 7.91
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: FLEURS (Spanish)
+      type: google/fleurs
+      config: es_419
+      split: test
+    metrics:
+    - name: WER (1.12s frame size, LangID)
+      type: wer
+      value: 4.11
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: FLEURS (French)
+      type: google/fleurs
+      config: fr_fr
+      split: test
+    metrics:
+    - name: WER (1.12s frame size, LangID)
+      type: wer
+      value: 9.03
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: FLEURS (Italian)
+      type: google/fleurs
+      config: it_it
+      split: test
+    metrics:
+    - name: WER (1.12s frame size, LangID)
+      type: wer
+      value: 4.25
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: FLEURS (Portuguese)
+      type: google/fleurs
+      config: pt_br
+      split: test
+    metrics:
+    - name: WER (1.12s frame size, LangID)
+      type: wer
+      value: 5.48
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: FLEURS (German)
+      type: google/fleurs
+      config: de_de
+      split: test
+    metrics:
+    - name: WER (1.12s frame size, LangID)
+      type: wer
+      value: 8.31
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: FLEURS (Hindi)
+      type: google/fleurs
+      config: hi_in
+      split: test
+    metrics:
+    - name: WER (1.12s frame size, LangID)
+      type: wer
+      value: 6.81
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: FLEURS (Korean)
+      type: google/fleurs
+      config: ko_kr
+      split: test
+    metrics:
+    - name: WER (1.12s frame size, LangID)
+      type: wer
+      value: 7.12
+metrics:
+- wer
+pipeline_tag: automatic-speech-recognition
 ---
 
-
-# Nemotron-3.5-asr-streaming-0.6b
+# Nemotron-3.5 ASR
 
 <style>
 h1, h2, h3, h4, h5, h6 {
@@ -31,134 +196,256 @@ img {
   display: inline;
   vertical-align: middle;
 }
-
-
 </style>
 
-[![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--CacheAware--RNNT-lightgrey#model-badge)](#model-architecture)
-| [![Model size](https://img.shields.io/badge/Params-600M-lightgrey#model-badge)](#model-architecture)
+<p align="center">
+  <a href="#model-architecture"><img src="https://img.shields.io/badge/Model_Arch-FastConformer--CacheAware--RNNT-lightgrey#model-badge" alt="Model architecture"/></a>
+  &nbsp;
+  <a href="#model-architecture"><img src="https://img.shields.io/badge/Params-600M-lightgrey#model-badge" alt="Model size"/></a>
+  &nbsp;
+  <a href="#supported-languages"><img src="https://img.shields.io/badge/Language-Multilingual-lightgrey#model-badge" alt="Language"/></a>
+</p>
+
+<p align="center">
+  <img src="model_overview.png" alt="Nemotron-3.5-ASR-Streaming overview: multilingual audio across 40 language-locales is transcribed by a cache-aware FastConformer-RNNT model with language-ID prompting into punctuated text with an automatic language tag" width="900"/>
+</p>
 
 > [!Note]
-> May 29, 2026: nemotron-3.5-asr-streaming-0.6b checkpoint was udpated.
+> This model is the multilingual extension of [nvidia/nemotron-speech-streaming-en-0.6b](https://huggingface.co/nvidia/nemotron-speech-streaming-en-0.6b), adding language-ID prompt conditioning to support transcription across **40 language-locales** from a single model.
 
+**Nemotron-3.5-ASR** is a multilingual, streaming Automatic Speech Recognition (ASR) model engineered to deliver high-quality multilingual transcription across both low-latency streaming and high-throughput batch workloads. Developed by NVIDIA, this 600M parameter model transcribes speech into text with native support for punctuation and capitalization, and offers runtime flexibility with configurable chunk sizes, including 80ms, 160ms, 320ms, 560ms, and 1120ms.
 
-# Model Overview
+By leveraging a state-of-the-art Cache-Aware FastConformer-RNNT architecture, the model eliminates redundant overlapping computations common in traditional "buffered" streaming. This allows it to process only new audio chunks while reusing cached encoder context, significantly improving computational efficiency and minimizing end-to-end delay without sacrificing accuracy.
 
+It was trained on a massive ASR dataset and is engineered to perform across diverse and challenging acoustic conditions.
 
-### Description:
-**Nemotron 3 ASR Multilingual (Nemotron-ASR-Streaming-Multilingual)** is a multilingual, streaming Automatic Speech Recognition (ASR) engineered to deliver high-quality multilingual transcription across both low-latency streaming and high-throughput batch workloads. Developed by NVIDIA, this 600M parameter model transcribes speech into text with native support for punctuation and capitalization. <br>
+This model is ready for commercial/non-commercial use.
 
-By leveraging a state-of-the-art **Cache-Aware FastConformer-RNNT** architecture, the model eliminates redundant overlapping computations common in traditional "buffered" streaming. This allows it to process only new audio chunks while reusing cached encoder context, significantly improving computational efficiency and minimizing end-to-end delay without sacrificing accuracy. <br>
+---
 
-The model transcribes the following languages: English (en-US, en-GB), Spanish (es-US, es-ES), German (de-DE), French (fr-FR, fr-CA), Italian (it-IT), Arabic (ar-AR), Japanese (ja-JP), Korean (ko-KR), Portuguese (pt-BR, pt-PT), Russian (ru-RU), Hindi (hi-IN), Mandarin (zh-CN), Vietnamese (vi-VN), Hebrew (he-IL), Dutch (nl-NL), Czech (cz-CZ), Danish (da-DK), Polish (pl-PL), Norwegian (nn-NO, nb-NO), Swedish (sv-SE), Thai (th-TH), Turkish (tr-TR), Bulgarian (bg-BG), Greek (el-GR), Estonian (et-EE), Finnish (fi-FI), Croatian (hr-HR), Hungarian (hu-HU), Lithuanian (lt-LT), Latvian (lv-LV), Romanian (ro-RO), Slovak (sk-SK), Ukrainian (uk-UA), Maltese (mt-MT), and Slovenian (sl-SL). It supports uppercase and lowercase letters, punctuation, spaces, and apostrophes.
+## License/Terms of Use
 
-It was trained on a massive ASR dataset of approximately 450,000 hours of speech and is engineered to perform across diverse and challenging acoustic conditions.
+Governing Terms: Use of the model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
 
-**This model is ready for commercial/non-commercial use.** <br>
+## Deployment Geography
 
-### License/Terms of Use:
-Governing Terms: Use of this model is governed by the [NVIDIA Model Evaluation License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-and-model-evaluation-license/).
+Global
 
-### Deployment Geography:
-Global <br>
+## Use Case
 
-### Use Case: <br>
-This model is for transcription of multilingual audio. <br>
+This model is for transcription of multilingual audio.
 
-### Release Date:  <br>
-Hugging Face [06/05/2026] via [https://huggingface.co/nvidia/nemotron-asr-streaming-multilingual-0.6b] <br> 
+## Release Date
 
-## References(s):
-[1] [Stateful Conformer with Cache-based Inference for Streaming Automatic Speech Recognition](https://arxiv.org/pdf/2312.17279) <br>
-[2] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084) <nr> 
+- Hugging Face [06/05/2026] via https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b
 
+## References
 
-## Model Architecture:
-**Architecture Type:** FastConformer-CacheAware-RNNT with Prompt <br>  
-The model is based on the Cache-Aware [1] FastConformer [2] architecture with 24 encoder layers and an RNNT (Recurrent Neural Network Transducer) decoder. The cache-aware streaming design enables efficient processing of audio in chunks while maintaining context from previous frames. Unlike buffered inference, this model maintains caches for all encoder self-attention and convolution layers. This enables reuse of hidden states at every streaming step, where cached activations eliminate redundant computations. As a result, there are no overlapping computations; each processed frame is strictly non-overlapping. This model leverages prompts to guide the transcription process, enabling language-specific transcription from a single ASR model through language ID conditioning.  <br>
+<a id="ref-1"></a>[1] [Stateful Conformer with Cache-based Inference for Streaming Automatic Speech Recognition](https://arxiv.org/abs/2312.17279)
 
-**Network Architecture:** 
-* Encoder: Cache-Aware FastConformer with 24 layers
-* Decoder: RNNT (Recurrent Neural Network Transducer)
+<a id="ref-2"></a>[2] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)
 
-**This model was developed based on [nvidia/nemotron-speech-streaming-en-0.6b](https://huggingface.co/nvidia/nemotron-speech-streaming-en-0.6b) [1]. <br>
+<a id="ref-3"></a>[3] [NVIDIA Granary](https://huggingface.co/datasets/nvidia/Granary)
 
-** Number of model parameters: 600M <br>
+<a id="ref-4"></a>[4] [NVIDIA NeMo Framework](https://github.com/NVIDIA/NeMo)
 
-## Input(s): <br>
-**Input Type(s):** Audio, Lang ID <br>
+## Why Choose Nemotron-3.5-ASR-Streaming?
 
-**Input Format(s):** wav, string <br>
+- 🌍 **Single Multilingual Model:** Transcribes 40 language-locales from one model through language-ID prompt conditioning, with optional automatic language detection.
+- ⚡ **Native Streaming Architecture:** Cache-aware design enables efficient processing of continuous audio streams, designed and optimized for low-latency voice agent applications.
+- 💰 **Improved Operational Efficiency:** Delivers superior throughput compared to traditional buffered streaming approaches. This allows for a higher number of parallel streams within the same GPU memory constraints, directly reducing operational costs for production environments.
+- 🎛️ **Dynamic Runtime Flexibility:** Choose the optimal operating point on the latency-accuracy Pareto curve at inference time. No re-training is required to adjust for different use-case requirements.
+- 📝 **Punctuation & Capitalization:** Built-in support for punctuation and capitalization in output text.
 
-**Input Parameters:** One-Dimensional (1D) for audio and One-Dimensional (1D) for Lang ID <br>
+---
 
-**Other Properties Related to Input:** Maximum Length in seconds specific to GPU Memory, No Pre-Processing Needed, Mono channel is required. 
+## Supported Languages
 
-Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>
+The model supports **40 language-locales** in total, across three tiers:
 
-## Output(s)
+- **Transcription-ready (19 locales):** highest-accuracy ASR, ready out of the box.
+- **Broad-coverage (13 locales):** production ASR across an additional 13 locales.
+- **Adaptation-ready (8 locales):** recognized by the tokenizer; fine-tune on in-domain data to unlock full transcription.
 
-**Output Type(s):** Text String in Input Language <br>
+| Tier | Languages (locales) |
+| :--- | :--- |
+| **Transcription-ready (19 locales)** | English (en-US, en-GB), Spanish (es-US, es-ES), French (fr-FR, fr-CA), Italian (it-IT), Portuguese (pt-BR, pt-PT), Dutch (nl-NL), German (de-DE), Turkish (tr-TR), Russian (ru-RU), Arabic (ar-AR), Hindi (hi-IN), Japanese (ja-JP), Korean (ko-KR), Vietnamese (vi-VN), Ukrainian (uk-UA) |
+| **Broad-coverage (13 locales)** | Polish (pl-PL), Swedish (sv-SE), Czech (cs-CZ), Norwegian Bokmål (nb-NO), Danish (da-DK), Bulgarian (bg-BG), Finnish (fi-FI), Croatian (hr-HR), Slovak (sk-SK), Mandarin (zh-CN), Hungarian (hu-HU), Romanian (ro-RO), Estonian (et-EE) |
+| **Adaptation-ready (8 locales)** | Greek (el-GR), Lithuanian (lt-LT), Latvian (lv-LV), Maltese (mt-MT), Slovenian (sl-SL), Hebrew (he-IL), Thai (th-TH), Norwegian Nynorsk (nn-NO) |
 
-**Output Format(s):** String <br>
+> **Note:** Transcription-ready and broad-coverage locales (**32 total**) produce ASR transcription out of the box; adaptation-ready locales require fine-tuning on in-domain data to enable full transcription. The model supports uppercase and lowercase letters, punctuation, spaces, and apostrophes.
 
-**Output Parameters:** One-Dimensional (1D) <br>
+> [!Tip]
+> **Automatic language detection / language tagging:** When run with `target_lang=auto`, the model detects the spoken language and emits the corresponding **language code/tag** in the output following the terminal punctuation. This lets a single deployment transcribe mixed-language traffic and automatically label each utterance with its detected language — no separate language-ID component required.
 
-**Other Properties Related to Output:** No Maximum Character Length, transcribe punctuation and capitalization. 
+---
+
+## Model Architecture
+
+**Architecture Type:** FastConformer-CacheAware-RNNT with Prompt
+
+This model consists of a cache-aware streaming Parakeet (FastConformer) encoder with an RNN-T decoder and language-ID prompt conditioning. It is based on the Cache-Aware [\[1\]](#ref-1) FastConformer [\[2\]](#ref-2) architecture with 24 encoder layers and an RNNT (Recurrent Neural Network Transducer) decoder. The cache-aware streaming design enables efficient processing of audio in chunks while maintaining context from previous frames. Unlike buffered inference, this model maintains caches for all encoder self-attention and convolution layers. This enables reuse of hidden states at every streaming step, where cached activations eliminate redundant computations. As a result, there are no overlapping computations; each processed frame is strictly non-overlapping. This model leverages prompts to guide the transcription process, enabling language-specific transcription from a single ASR model through language ID conditioning.
 
-Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>
+<p align="center">
+  <img src="model_architecture.png" alt="Nemotron-3.5-ASR-Streaming architecture: FastConformer encoder and language-ID encoding are concatenated, projected, and fed to the RNNT decoder" width="900"/>
+</p>
 
+The language-ID prompt is fused with the acoustic representation as follows:
+
+- **FastConformer encoder** processes audio into an acoustic embedding of shape (D=512, T).
+- **Language Encoding** expands a 128-dim one-hot language vector across the time axis → (K=128, T), broadcasting the language identity to every frame.
+- **Concatenation** along the feature axis → fused tensor (D + K, T).
+- **Projection layer** maps the fused features to the RNNT decoder.
+
+**Network Architecture:**
+- Encoder: Cache-Aware FastConformer with 24 layers
+- Decoder: RNNT (Recurrent Neural Network Transducer)
+- Parameters: 600M
+
+**This model was developed based on [nvidia/nemotron-speech-streaming-en-0.6b](https://huggingface.co/nvidia/nemotron-speech-streaming-en-0.6b).**
+
+---
 
-## How to use this model
+## Results at a Glance
 
-**NeMo-framework (EA):** <br>
+ASR performance is measured using Word Error Rate (WER) on the **FLEURS** test sets. Accuracy stays strong across both modes and improves as the chunk size grows, while remaining competitive even at the lowest-latency 80ms setting. Full tables are in [Performance](#performance).
+
+<table>
+<tr>
+<td width="50%"><img src="fleurs_wer_vs_chunk_size.png" alt="FLEURS average WER vs streaming chunk size (LangID vs Auto-detect)"/></td>
+<td width="50%"><img src="fleurs_langid_vs_auto.png" alt="FLEURS WER by language: LangID vs Auto-detect at 320ms chunk"/></td>
+</tr>
+</table>
+
+> **Note:** Japanese and Korean are measured using Character Error Rate (CER) rather than WER, as is standard for these languages.
+
+---
+
+## Throughput & Efficiency
+
+Despite being **roughly half the size** (0.6B vs. 1.1B), Nemotron-3.5-ASR-Streaming serves **far more concurrent streams at far lower latency** than the [Parakeet RNNT 1.1B multilingual model](https://build.nvidia.com/nvidia/parakeet-1_1b-rnnt-multilingual-asr), which runs on buffered streaming. The cache-aware streaming design avoids the redundant recomputation of buffered inference, so a single H100 can sustain dramatically higher concurrency at every chunk size — directly lowering the cost per stream in production. At the lowest-latency 80ms setting, Nemotron sustains **~17× more concurrent streams** (240 vs. 14); at the 1120ms setting it sustains **6× more** (2,400 vs. 400). The latency-vs-concurrency curves tell the same story: Nemotron (solid green) holds low final-token latency well past 1,000 parallel requests, while Parakeet RNNT 1.1B (dashed blue) saturates after only a few hundred.
+
+<table>
+<tr>
+<td width="50%"><img src="throughput_vs_chunk.png" alt="Concurrent streams supported on a single H100: Nemotron ASR streaming vs Parakeet RNNT, across chunk sizes"/></td>
+<td width="50%"><img src="latency_vs_parallel.png" alt="Median final-token latency vs number of parallel requests on a single H100, Nemotron vs Parakeet RNNT across chunk sizes"/></td>
+</tr>
+</table>
+
+> Measured on a single NVIDIA H100. Throughput is the number of real-time streams sustainable in parallel; latency is the median final-token latency at a given level of concurrency.
+
+---
+
+## Explore more from NVIDIA
+
+For documentation, deployment guides, enterprise-ready APIs, and the latest open models—including Nemotron and other cutting-edge speech, translation, and generative AI—visit the NVIDIA Developer Portal at [developer.nvidia.com](https://developer.nvidia.com/).
+Join the community to access tools, support, and resources to accelerate your development with NVIDIA's NeMo, Riva, NIM, and foundation models.
+
+- What is [Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/)?
+- NVIDIA Developer [Nemotron](https://developer.nvidia.com/nemotron)
+- [NVIDIA Riva Speech](https://developer.nvidia.com/riva?sortBy=developer_learning_library%2Fsort%2Ffeatured_in.riva%3Adesc%2Ctitle%3Aasc#demos)
+- [NeMo Documentation](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/models.html)
+
+Also, check out the following NVIDIA speech models:
+- Nemotron Speech Streaming (English) - https://huggingface.co/nvidia/nemotron-speech-streaming-en-0.6b
+- Multitalker Parakeet Streaming - https://huggingface.co/nvidia/multitalker-parakeet-streaming-0.6b-v1
+- Parakeet Realtime EOU - https://huggingface.co/nvidia/parakeet_realtime_eou_120m-v1
+
+---
+
+## NVIDIA NeMo
+
+To train, fine-tune or perform inference with this model, you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) [\[4\]](#ref-4). We recommend you install it after you've installed Cython and latest PyTorch version.
 
 ```bash
-git clone https://github.com/kingformatty/NeMo.git
-git checkout prompt_unitifed_architecture_hf_EA
+apt-get update && apt-get install -y libsndfile1 ffmpeg
+pip install Cython packaging
+pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[asr]
 ```
 
-**Streaming inference with target language:** <br>
+## How to Use this Model
 
-Supported att_context_size: [56,0], [56,1], [56,3] (Default), [56,6], [56,13]
+The model is available for use in the NeMo Framework, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
 
-```bash
-python ${NEMO_ROOT}/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py \
-    model_path=${MODEL_PATH} \
-    dataset_manifest=${MANIFEST_PATH} \
-    output_path=${OUTPUT_FOLDER} \
-    target_lang=es-ES \
-    att_context_size="[56,3]" \
-    strip_lang_tags=true
+### Loading the Model
+
+```python
+import nemo.collections.asr as nemo_asr
+asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name="nvidia/nemotron-3.5-asr-streaming-0.6b")
 ```
-(es-ES for example, supported languages can be found in model description)
 
-**Streaming inference with auto model (agnostic language):** <br>
+### Streaming Inference
+
+You can use the cache-aware streaming inference script from NeMo - [NeMo/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py](https://github.com/NVIDIA-NeMo/NeMo/blob/main/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py)
+
+This is a prompt-conditioned multilingual model: pass the target language with `target_lang` (e.g. `en-US`, `es-ES`, `de-DE`), or use `target_lang=auto` for automatic language detection.
 
 ```bash
-python ${NEMO_ROOT}/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py \
-    model_path=${MODEL_PATH} \
-    dataset_manifest=${MANIFEST_PATH} \
-    output_path=${OUTPUT_FOLDER} \
-    target_lang=auto \
-    att_context_size="[56,3]" \
-    strip_lang_tags=true
+cd NeMo
+python examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py \
+    model_path=<model_path> \
+    dataset_manifest=<dataset_manifest> \
+    batch_size=<batch_size> \
+    target_lang=<lang_id> \ #language key (e.g. en-US) or "auto" for automatic language detection
+    att_context_size="[56,13]" \ #set the second value to the desired right context from {0,1,3,6,13}
+    strip_lang_tags=true \ #true: remove the detected language tag from the text; false: keep it in the output
+    output_path=<output_folder>
 ```
 
+**`strip_lang_tags`** controls how the detected language tag is handled in the output. The model appends a language tag (e.g. `<en-US>`) after the transcript's terminal punctuation:
+- `strip_lang_tags=false` (keep): the tag is left in the output, so you can read the detected language directly from each utterance — useful for mixed-language traffic and language labeling.
+- `strip_lang_tags=true` (remove): the tag is stripped, leaving only the clean transcript text — useful when you only need the spoken words.
 
-## Software Integration:
-**Runtime Engine(s):** 
-* NeMo 25.11, Riva 2.25.0 or higher <br>
+### Setting up Streaming Configuration
 
-**Supported Hardware Microarchitecture Compatibility:** <br>
-* NVIDIA Ampere <br>
-* NVIDIA Blackwell <br>
-* NVIDIA Jetson <br>
-* NVIDIA Hopper <br>
-* NVIDIA Lovelace <br>
-* NVIDIA Turing <br>
-* NVIDIA Volta <br>
+Latency is defined by the `att_context_size` param, where att_context_size = `{num_frames_left_context, num_frame_right_context}`, all measured in **80ms frames**:
+
+* [56, 0]: Chunk size = 1 (1 × 80ms = 0.08s)
+* [56, 1]: Chunk size = 2 (2 × 80ms = 0.16s)
+* [56, 3]: Chunk size = 4 (4 × 80ms = 0.32s)
+* [56, 6]: Chunk size = 7 (7 × 80ms = 0.56s)
+* [56, 13]: Chunk size = 14 (14 × 80ms = 1.12s)
+
+Here, chunk size = current frame + right context; each chunk is processed in non-overlapping fashion.
+
+### Input(s): <br>
+
+**Input Type(s):** Audio, Lang ID <br>
+
+**Input Format(s):** wav, string <br>
+
+**Input Parameters:** One-Dimensional (1D) for audio and One-Dimensional (1D) for Lang ID <br>
+
+**Other Properties Related to Input:** Maximum Length in seconds specific to GPU Memory, No Pre-Processing Needed, Mono channel is required. 
 
+By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>
+
+### Output
+
+**Output Type(s):** Text String in Input Language <br>
+
+**Output Format(s):** String <br>
+
+**Output Parameters:** One-Dimensional (1D) <br>
+
+**Other Properties Related to Output:** No Maximum Character Length, transcribe punctuation and capitalization. 
+
+By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>
+
+---
+
+## Software Integration
+
+**Runtime Engine:** NeMo 26.06
+
+**Supported Hardware Microarchitecture Compatibility:**
+- NVIDIA Ampere
+- NVIDIA Blackwell
+- NVIDIA Hopper
+- NVIDIA Jetson
+- NVIDIA Lovelace
+- NVIDIA Turing
+- NVIDIA Volta
 
 **Supported Operating System(s):**
 * Linux <br>
@@ -166,12 +453,27 @@ python ${NEMO_ROOT}/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_
 
 The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.<vr>
 
+
+
+---
+
+
 ## Model Version(s):
-nemotron-asr-streaming-multilingual-v1  <br>
+nemotron-3.5-asr <br>
 
 ## Training and Evaluation Datasets:
 
-## Training Dataset
+### Training Datasets
+
+It was trained on speech data across 40 language-locales. The training data is a dynamic blend of public and proprietary internal datasets normalized to have spoken forms in text with punctuation and capitalization, including:
+
+
+- NVIDIA Riva multilingual ASR training set (Proprietary)
+- NVIDIA Granary [\[3\]](#ref-3)
+- Multilingual LibriSpeech (MLS)
+- Mozilla Common Voice
+- FLEURS
+- VoxPopuli / Europarl-ASR
 
 ** Data Modality: Audio <br>
 
@@ -182,21 +484,21 @@ nemotron-asr-streaming-multilingual-v1  <br>
 
 ** Labeling Method by dataset <br>
 * Human <br>
-* Synthetic: labels generated from Parakeet-CTC-XXL-1.1b model, PnC generated from [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) <br>
+* Synthetic: Synthetic labels were generated from an ensemble of ASR models ([NVIDIA Canary](https://huggingface.co/nvidia/canary-1b), [Parakeet Multilingual 1.1B RNNT](https://build.nvidia.com/nvidia/parakeet-1_1b-rnnt-multilingual-asr), [Parakeet CTC 1.1B](https://build.nvidia.com/nvidia/parakeet-ctc-1_1b-asr), [OpenAI Whisper](https://huggingface.co/openai/whisper-large-v3), and [FunASR](https://github.com/modelscope/FunASR)), with punctuation and capitalization (PnC) generated from [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B).
 
-**Properties:** 
-In excess of 450,000 hours of speech across the following languages: English (en-US, en-GB), Spanish (es-US, es-ES), German (de-DE), French (fr-FR, fr-CA), Italian (it-IT), Arabic (ar-AR), Japanese (ja-JP), Korean (ko-KR), Portuguese (pt-BR, pt-PT), Russian (ru-RU), Hindi (hi-IN), Mandarin (zh-CN), Vietnamese (vi-VN), Hebrew (he-IL), Dutch (nl-NL), Czech (cz-CZ), Danish (da-DK), Polish (pl-PL), Norwegian (nn-NO, nb-NO), Swedish (sv-SE), Thai (th-TH), Turkish (tr-TR), Bulgarian (bg-BG), Greek (el-GR), Estonian (et-EE), Finnish (fi-FI), Croatian (hr-HR), Hungarian (hu-HU), Lithuanian (lt-LT), Latvian (lv-LV), Romanian (ro-RO), Slovak (sk-SK), Ukrainian (uk-UA), Maltese (mt-MT), and Slovenian (sl-SL). The training data is a dynamic blend of public and proprietary internal datasets normalized to have spoken forms in text with punctuation and capitalization. <br>
 
-## Evaluation Dataset
 
-** Data Collection Method by dataset <br>
-* Human <br>
 
-** Labeling Method by dataset <br>
-* Human <br>
+### Evaluation Datasets
 
-**Properties:** A dynamic blend of public and internal proprietary normalized to spoken forms in text with punctuation and capitalization. <br>
+The model was evaluated on multilingual ASR benchmarks:
 
+- FLEURS
+- Mozilla Common Voice (MCV)
+- Multilingual LibriSpeech (MLS)
+- NVIDIA internal multilingual evaluation sets
+
+---
 
 # Inference:
 **Acceleration Engine:** Triton <br>
@@ -210,15 +512,85 @@ In excess of 450,000 hours of speech across the following languages: English (en
 * NVIDIA L40 <br>
 
 
-## Ethical Considerations:
-NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br> 
+## Performance
+
+ASR performance is measured using the Word Error Rate (WER). The tables below report WER (%) on the **FLEURS** test sets across configurable streaming chunk sizes, in two modes:
+- **Language Input (LangID):** the target language is provided to the model.
+- **Auto-detect:** the model automatically detects the spoken language.
+
+> **Note:** Japanese, Korean, and Mandarin are evaluated using Character Error Rate (CER) rather than WER, as is standard for these languages.
+> **Note on text normalization:** WER/CER are computed after text normalization that aligns the reference and hypothesis (e.g., casing, punctuation, numerals, and formatting conventions). Normalization is not perfect across all 40 language-locales, and residual mismatches between normalized text can inflate the reported error rates — actual transcription quality may be somewhat better than the numbers suggest.
+
+### Transcription-ready (19 locales)
+
+_Languages are ordered by accuracy (lowest WER first)._
+
+<table>
+  <thead>
+    <tr><th rowspan="2" align="left">Language</th><th colspan="5" align="center" style="background-color:#76b900;color:#ffffff">Language Input (LangID)</th><th colspan="5" align="center" style="background-color:#6b7280;color:#ffffff;border-left:2px solid #cbd5e1;">Auto-detect</th></tr>
+    <tr><th align="center" style="background-color:#eef6e0">80ms</th><th align="center" style="background-color:#eef6e0">160ms</th><th align="center" style="background-color:#eef6e0">320ms</th><th align="center" style="background-color:#eef6e0">560ms</th><th align="center" style="background-color:#eef6e0">1.12s</th><th align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">80ms</th><th align="center" style="background-color:#f3f4f6;">160ms</th><th align="center" style="background-color:#f3f4f6;">320ms</th><th align="center" style="background-color:#f3f4f6;">560ms</th><th align="center" style="background-color:#f3f4f6;">1.12s</th></tr>
+  </thead>
+  <tbody>
+    <tr><td align="left">Spanish (es-US, es-ES)</td><td align="center" style="background-color:#eef6e0;">4.87</td><td align="center" style="background-color:#eef6e0;">4.64</td><td align="center" style="background-color:#eef6e0;">4.39</td><td align="center" style="background-color:#eef6e0;">4.26</td><td align="center" style="background-color:#eef6e0;">4.11</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">5.04</td><td align="center" style="background-color:#f3f4f6;">4.82</td><td align="center" style="background-color:#f3f4f6;">4.48</td><td align="center" style="background-color:#f3f4f6;">4.34</td><td align="center" style="background-color:#f3f4f6;">4.13</td></tr>
+    <tr><td align="left">Italian (it-IT)</td><td align="center" style="background-color:#eef6e0;">5.23</td><td align="center" style="background-color:#eef6e0;">4.85</td><td align="center" style="background-color:#eef6e0;">4.83</td><td align="center" style="background-color:#eef6e0;">4.41</td><td align="center" style="background-color:#eef6e0;">4.25</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">5.28</td><td align="center" style="background-color:#f3f4f6;">4.89</td><td align="center" style="background-color:#f3f4f6;">4.84</td><td align="center" style="background-color:#f3f4f6;">4.47</td><td align="center" style="background-color:#f3f4f6;">4.32</td></tr>
+    <tr><td align="left">Portuguese (pt-BR, pt-PT)</td><td align="center" style="background-color:#eef6e0;">6.29</td><td align="center" style="background-color:#eef6e0;">6.10</td><td align="center" style="background-color:#eef6e0;">5.81</td><td align="center" style="background-color:#eef6e0;">5.65</td><td align="center" style="background-color:#eef6e0;">5.48</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">6.41</td><td align="center" style="background-color:#f3f4f6;">6.19</td><td align="center" style="background-color:#f3f4f6;">5.82</td><td align="center" style="background-color:#f3f4f6;">5.57</td><td align="center" style="background-color:#f3f4f6;">5.47</td></tr>
+    <tr><td align="left">Hindi (hi-IN)</td><td align="center" style="background-color:#eef6e0;">8.13</td><td align="center" style="background-color:#eef6e0;">7.97</td><td align="center" style="background-color:#eef6e0;">7.41</td><td align="center" style="background-color:#eef6e0;">7.05</td><td align="center" style="background-color:#eef6e0;">6.81</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">11.47</td><td align="center" style="background-color:#f3f4f6;">10.83</td><td align="center" style="background-color:#f3f4f6;">9.88</td><td align="center" style="background-color:#f3f4f6;">9.26</td><td align="center" style="background-color:#f3f4f6;">8.23</td></tr>
+    <tr><td align="left">Korean (ko-KR)</td><td align="center" style="background-color:#eef6e0;">7.59</td><td align="center" style="background-color:#eef6e0;">7.70</td><td align="center" style="background-color:#eef6e0;">7.27</td><td align="center" style="background-color:#eef6e0;">7.18</td><td align="center" style="background-color:#eef6e0;">7.12</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">8.31</td><td align="center" style="background-color:#f3f4f6;">8.18</td><td align="center" style="background-color:#f3f4f6;">7.81</td><td align="center" style="background-color:#f3f4f6;">7.49</td><td align="center" style="background-color:#f3f4f6;">7.30</td></tr>
+    <tr><td align="left">English (en-US, en-GB)</td><td align="center" style="background-color:#eef6e0;">9.43</td><td align="center" style="background-color:#eef6e0;">8.88</td><td align="center" style="background-color:#eef6e0;">8.27</td><td align="center" style="background-color:#eef6e0;">7.99</td><td align="center" style="background-color:#eef6e0;">7.91</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">9.72</td><td align="center" style="background-color:#f3f4f6;">9.34</td><td align="center" style="background-color:#f3f4f6;">8.84</td><td align="center" style="background-color:#f3f4f6;">8.80</td><td align="center" style="background-color:#f3f4f6;">8.84</td></tr>
+    <tr><td align="left">German (de-DE)</td><td align="center" style="background-color:#eef6e0;">9.81</td><td align="center" style="background-color:#eef6e0;">9.21</td><td align="center" style="background-color:#eef6e0;">8.83</td><td align="center" style="background-color:#eef6e0;">8.42</td><td align="center" style="background-color:#eef6e0;">8.31</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">9.90</td><td align="center" style="background-color:#f3f4f6;">9.37</td><td align="center" style="background-color:#f3f4f6;">8.87</td><td align="center" style="background-color:#f3f4f6;">8.58</td><td align="center" style="background-color:#f3f4f6;">8.22</td></tr>
+    <tr><td align="left">French (fr-FR, fr-CA)</td><td align="center" style="background-color:#eef6e0;">10.97</td><td align="center" style="background-color:#eef6e0;">10.60</td><td align="center" style="background-color:#eef6e0;">9.79</td><td align="center" style="background-color:#eef6e0;">9.45</td><td align="center" style="background-color:#eef6e0;">9.03</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">11.03</td><td align="center" style="background-color:#f3f4f6;">10.60</td><td align="center" style="background-color:#f3f4f6;">9.84</td><td align="center" style="background-color:#f3f4f6;">9.46</td><td align="center" style="background-color:#f3f4f6;">9.02</td></tr>
+    <tr><td align="left">Russian (ru-RU)</td><td align="center" style="background-color:#eef6e0;">10.84</td><td align="center" style="background-color:#eef6e0;">10.73</td><td align="center" style="background-color:#eef6e0;">9.87</td><td align="center" style="background-color:#eef6e0;">9.60</td><td align="center" style="background-color:#eef6e0;">9.17</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">12.47</td><td align="center" style="background-color:#f3f4f6;">12.09</td><td align="center" style="background-color:#f3f4f6;">11.01</td><td align="center" style="background-color:#f3f4f6;">10.57</td><td align="center" style="background-color:#f3f4f6;">10.03</td></tr>
+    <tr><td align="left">Turkish (tr-TR)</td><td align="center" style="background-color:#eef6e0;">12.34</td><td align="center" style="background-color:#eef6e0;">12.33</td><td align="center" style="background-color:#eef6e0;">12.05</td><td align="center" style="background-color:#eef6e0;">11.34</td><td align="center" style="background-color:#eef6e0;">11.17</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">12.61</td><td align="center" style="background-color:#f3f4f6;">12.28</td><td align="center" style="background-color:#f3f4f6;">11.93</td><td align="center" style="background-color:#f3f4f6;">11.51</td><td align="center" style="background-color:#f3f4f6;">11.32</td></tr>
+    <tr><td align="left">Vietnamese (vi-VN)</td><td align="center" style="background-color:#eef6e0;">13.41</td><td align="center" style="background-color:#eef6e0;">12.87</td><td align="center" style="background-color:#eef6e0;">12.29</td><td align="center" style="background-color:#eef6e0;">11.78</td><td align="center" style="background-color:#eef6e0;">11.18</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">13.59</td><td align="center" style="background-color:#f3f4f6;">13.02</td><td align="center" style="background-color:#f3f4f6;">12.40</td><td align="center" style="background-color:#f3f4f6;">12.02</td><td align="center" style="background-color:#f3f4f6;">11.22</td></tr>
+    <tr><td align="left">Dutch (nl-NL)</td><td align="center" style="background-color:#eef6e0;">14.03</td><td align="center" style="background-color:#eef6e0;">13.43</td><td align="center" style="background-color:#eef6e0;">12.17</td><td align="center" style="background-color:#eef6e0;">11.97</td><td align="center" style="background-color:#eef6e0;">11.46</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">14.09</td><td align="center" style="background-color:#f3f4f6;">13.80</td><td align="center" style="background-color:#f3f4f6;">12.62</td><td align="center" style="background-color:#f3f4f6;">12.24</td><td align="center" style="background-color:#f3f4f6;">11.70</td></tr>
+    <tr><td align="left">Japanese (ja-JP)</td><td align="center" style="background-color:#eef6e0;">13.87</td><td align="center" style="background-color:#eef6e0;">12.90</td><td align="center" style="background-color:#eef6e0;">12.22</td><td align="center" style="background-color:#eef6e0;">11.91</td><td align="center" style="background-color:#eef6e0;">11.48</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">14.97</td><td align="center" style="background-color:#f3f4f6;">13.85</td><td align="center" style="background-color:#f3f4f6;">13.00</td><td align="center" style="background-color:#f3f4f6;">12.38</td><td align="center" style="background-color:#f3f4f6;">11.66</td></tr>
+    <tr><td align="left">Arabic (ar-AR)</td><td align="center" style="background-color:#eef6e0;">13.17</td><td align="center" style="background-color:#eef6e0;">12.65</td><td align="center" style="background-color:#eef6e0;">12.55</td><td align="center" style="background-color:#eef6e0;">12.13</td><td align="center" style="background-color:#eef6e0;">12.03</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">13.47</td><td align="center" style="background-color:#f3f4f6;">12.85</td><td align="center" style="background-color:#f3f4f6;">12.67</td><td align="center" style="background-color:#f3f4f6;">12.18</td><td align="center" style="background-color:#f3f4f6;">12.06</td></tr>
+    <tr><td align="left">Ukrainian (uk-UA)</td><td align="center" style="background-color:#eef6e0;">15.70</td><td align="center" style="background-color:#eef6e0;">15.21</td><td align="center" style="background-color:#eef6e0;">14.55</td><td align="center" style="background-color:#eef6e0;">13.67</td><td align="center" style="background-color:#eef6e0;">13.07</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">18.81</td><td align="center" style="background-color:#f3f4f6;">17.96</td><td align="center" style="background-color:#f3f4f6;">16.79</td><td align="center" style="background-color:#f3f4f6;">15.60</td><td align="center" style="background-color:#f3f4f6;">14.59</td></tr>
+    <tr><td align="left"><strong>Average</strong></td><td align="center" style="background-color:#eef6e0;"><strong>10.38</strong></td><td align="center" style="background-color:#eef6e0;"><strong>10.00</strong></td><td align="center" style="background-color:#eef6e0;"><strong>9.49</strong></td><td align="center" style="background-color:#eef6e0;"><strong>9.12</strong></td><td align="center" style="background-color:#eef6e0;"><strong>8.84</strong></td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;"><strong>11.14</strong></td><td align="center" style="background-color:#f3f4f6;"><strong>10.67</strong></td><td align="center" style="background-color:#f3f4f6;"><strong>10.05</strong></td><td align="center" style="background-color:#f3f4f6;"><strong>9.63</strong></td><td align="center" style="background-color:#f3f4f6;"><strong>9.21</strong></td></tr>
+  </tbody>
+</table>
+
+### Broad-coverage (13 locales)
+
+_Languages are ordered by accuracy (lowest WER first)._
+
+<table>
+  <thead>
+    <tr><th rowspan="2" align="left">Language</th><th colspan="5" align="center" style="background-color:#76b900;color:#ffffff">Language Input (LangID)</th><th colspan="5" align="center" style="background-color:#6b7280;color:#ffffff;border-left:2px solid #cbd5e1;">Auto-detect</th></tr>
+    <tr><th align="center" style="background-color:#eef6e0">80ms</th><th align="center" style="background-color:#eef6e0">160ms</th><th align="center" style="background-color:#eef6e0">320ms</th><th align="center" style="background-color:#eef6e0">560ms</th><th align="center" style="background-color:#eef6e0">1.12s</th><th align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">80ms</th><th align="center" style="background-color:#f3f4f6;">160ms</th><th align="center" style="background-color:#f3f4f6;">320ms</th><th align="center" style="background-color:#f3f4f6;">560ms</th><th align="center" style="background-color:#f3f4f6;">1.12s</th></tr>
+  </thead>
+  <tbody>
+    <tr><td align="left">Polish</td><td align="center" style="background-color:#eef6e0;">19.88</td><td align="center" style="background-color:#eef6e0;">18.92</td><td align="center" style="background-color:#eef6e0;">17.48</td><td align="center" style="background-color:#eef6e0;">16.61</td><td align="center" style="background-color:#eef6e0;">15.15</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">22.65</td><td align="center" style="background-color:#f3f4f6;">21.63</td><td align="center" style="background-color:#f3f4f6;">20.05</td><td align="center" style="background-color:#f3f4f6;">18.52</td><td align="center" style="background-color:#f3f4f6;">16.55</td></tr>
+    <tr><td align="left">Norwegian Bokmål</td><td align="center" style="background-color:#eef6e0;">20.43</td><td align="center" style="background-color:#eef6e0;">20.07</td><td align="center" style="background-color:#eef6e0;">18.90</td><td align="center" style="background-color:#eef6e0;">18.44</td><td align="center" style="background-color:#eef6e0;">18.10</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">20.91</td><td align="center" style="background-color:#f3f4f6;">20.19</td><td align="center" style="background-color:#f3f4f6;">19.29</td><td align="center" style="background-color:#f3f4f6;">18.76</td><td align="center" style="background-color:#f3f4f6;">18.01</td></tr>
+    <tr><td align="left">Finnish</td><td align="center" style="background-color:#eef6e0;">21.19</td><td align="center" style="background-color:#eef6e0;">20.57</td><td align="center" style="background-color:#eef6e0;">20.05</td><td align="center" style="background-color:#eef6e0;">18.94</td><td align="center" style="background-color:#eef6e0;">18.34</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">21.61</td><td align="center" style="background-color:#f3f4f6;">20.88</td><td align="center" style="background-color:#f3f4f6;">20.40</td><td align="center" style="background-color:#f3f4f6;">19.36</td><td align="center" style="background-color:#f3f4f6;">18.72</td></tr>
+    <tr><td align="left">Mandarin</td><td align="center" style="background-color:#eef6e0;">20.56</td><td align="center" style="background-color:#eef6e0;">20.22</td><td align="center" style="background-color:#eef6e0;">20.03</td><td align="center" style="background-color:#eef6e0;">19.51</td><td align="center" style="background-color:#eef6e0;">19.28</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">22.45</td><td align="center" style="background-color:#f3f4f6;">21.07</td><td align="center" style="background-color:#f3f4f6;">20.59</td><td align="center" style="background-color:#f3f4f6;">20.40</td><td align="center" style="background-color:#f3f4f6;">19.87</td></tr>
+    <tr><td align="left">Czech</td><td align="center" style="background-color:#eef6e0;">24.18</td><td align="center" style="background-color:#eef6e0;">23.20</td><td align="center" style="background-color:#eef6e0;">22.41</td><td align="center" style="background-color:#eef6e0;">21.04</td><td align="center" style="background-color:#eef6e0;">20.41</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">25.81</td><td align="center" style="background-color:#f3f4f6;">25.12</td><td align="center" style="background-color:#f3f4f6;">23.68</td><td align="center" style="background-color:#f3f4f6;">22.55</td><td align="center" style="background-color:#f3f4f6;">21.45</td></tr>
+    <tr><td align="left">Bulgarian</td><td align="center" style="background-color:#eef6e0;">24.50</td><td align="center" style="background-color:#eef6e0;">23.58</td><td align="center" style="background-color:#eef6e0;">22.80</td><td align="center" style="background-color:#eef6e0;">21.70</td><td align="center" style="background-color:#eef6e0;">20.53</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">28.28</td><td align="center" style="background-color:#f3f4f6;">27.22</td><td align="center" style="background-color:#f3f4f6;">25.54</td><td align="center" style="background-color:#f3f4f6;">24.05</td><td align="center" style="background-color:#f3f4f6;">21.84</td></tr>
+    <tr><td align="left">Slovak</td><td align="center" style="background-color:#eef6e0;">25.08</td><td align="center" style="background-color:#eef6e0;">24.14</td><td align="center" style="background-color:#eef6e0;">23.73</td><td align="center" style="background-color:#eef6e0;">22.51</td><td align="center" style="background-color:#eef6e0;">21.28</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">27.59</td><td align="center" style="background-color:#f3f4f6;">26.06</td><td align="center" style="background-color:#f3f4f6;">25.61</td><td align="center" style="background-color:#f3f4f6;">24.15</td><td align="center" style="background-color:#f3f4f6;">22.68</td></tr>
+    <tr><td align="left">Swedish</td><td align="center" style="background-color:#eef6e0;">25.61</td><td align="center" style="background-color:#eef6e0;">24.85</td><td align="center" style="background-color:#eef6e0;">23.63</td><td align="center" style="background-color:#eef6e0;">22.72</td><td align="center" style="background-color:#eef6e0;">22.17</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">26.28</td><td align="center" style="background-color:#f3f4f6;">25.56</td><td align="center" style="background-color:#f3f4f6;">24.18</td><td align="center" style="background-color:#f3f4f6;">23.57</td><td align="center" style="background-color:#f3f4f6;">22.53</td></tr>
+    <tr><td align="left">Croatian</td><td align="center" style="background-color:#eef6e0;">27.92</td><td align="center" style="background-color:#eef6e0;">27.09</td><td align="center" style="background-color:#eef6e0;">25.79</td><td align="center" style="background-color:#eef6e0;">24.92</td><td align="center" style="background-color:#eef6e0;">23.97</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">32.13</td><td align="center" style="background-color:#f3f4f6;">31.20</td><td align="center" style="background-color:#f3f4f6;">29.65</td><td align="center" style="background-color:#f3f4f6;">28.95</td><td align="center" style="background-color:#f3f4f6;">27.46</td></tr>
+    <tr><td align="left">Romanian</td><td align="center" style="background-color:#eef6e0;">31.52</td><td align="center" style="background-color:#eef6e0;">30.93</td><td align="center" style="background-color:#eef6e0;">29.04</td><td align="center" style="background-color:#eef6e0;">27.77</td><td align="center" style="background-color:#eef6e0;">25.90</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">34.22</td><td align="center" style="background-color:#f3f4f6;">33.26</td><td align="center" style="background-color:#f3f4f6;">30.97</td><td align="center" style="background-color:#f3f4f6;">29.84</td><td align="center" style="background-color:#f3f4f6;">26.88</td></tr>
+    <tr><td align="left">Estonian</td><td align="center" style="background-color:#eef6e0;">29.95</td><td align="center" style="background-color:#eef6e0;">29.66</td><td align="center" style="background-color:#eef6e0;">28.59</td><td align="center" style="background-color:#eef6e0;">27.37</td><td align="center" style="background-color:#eef6e0;">26.35</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">30.58</td><td align="center" style="background-color:#f3f4f6;">30.09</td><td align="center" style="background-color:#f3f4f6;">28.72</td><td align="center" style="background-color:#f3f4f6;">28.03</td><td align="center" style="background-color:#f3f4f6;">27.19</td></tr>
+    <tr><td align="left">Danish</td><td align="center" style="background-color:#eef6e0;">32.62</td><td align="center" style="background-color:#eef6e0;">31.51</td><td align="center" style="background-color:#eef6e0;">30.00</td><td align="center" style="background-color:#eef6e0;">28.92</td><td align="center" style="background-color:#eef6e0;">27.49</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">33.15</td><td align="center" style="background-color:#f3f4f6;">31.77</td><td align="center" style="background-color:#f3f4f6;">30.22</td><td align="center" style="background-color:#f3f4f6;">29.33</td><td align="center" style="background-color:#f3f4f6;">27.81</td></tr>
+    <tr><td align="left">Hungarian</td><td align="center" style="background-color:#eef6e0;">32.70</td><td align="center" style="background-color:#eef6e0;">32.03</td><td align="center" style="background-color:#eef6e0;">30.92</td><td align="center" style="background-color:#eef6e0;">29.72</td><td align="center" style="background-color:#eef6e0;">28.68</td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;">33.40</td><td align="center" style="background-color:#f3f4f6;">32.39</td><td align="center" style="background-color:#f3f4f6;">31.49</td><td align="center" style="background-color:#f3f4f6;">30.20</td><td align="center" style="background-color:#f3f4f6;">29.18</td></tr>
+    <tr><td align="left"><strong>Average</strong></td><td align="center" style="background-color:#eef6e0;"><strong>25.86</strong></td><td align="center" style="background-color:#eef6e0;"><strong>25.14</strong></td><td align="center" style="background-color:#eef6e0;"><strong>24.11</strong></td><td align="center" style="background-color:#eef6e0;"><strong>23.09</strong></td><td align="center" style="background-color:#eef6e0;"><strong>22.13</strong></td><td align="center" style="background-color:#f3f4f6;border-left:2px solid #cbd5e1;"><strong>27.62</strong></td><td align="center" style="background-color:#f3f4f6;"><strong>26.65</strong></td><td align="center" style="background-color:#f3f4f6;"><strong>25.41</strong></td><td align="center" style="background-color:#f3f4f6;"><strong>24.44</strong></td><td align="center" style="background-color:#f3f4f6;"><strong>23.09</strong></td></tr>
+  </tbody>
+</table>
+
+### Adaptation-ready languages (fine-tune to enable)
+
+These **8 language-locales** are recognized by the tokenizer but are not tuned for production transcription out of the box: **Greek (el-GR), Hebrew (he-IL), Lithuanian (lt-LT), Slovenian (sl-SL), Latvian (lv-LV), Maltese (mt-MT), Thai (th-TH), and Norwegian Nynorsk (nn-NO)**. Fine-tuning on in-domain data is recommended to bring them to production quality.
+
+We are preparing a dedicated blog post that walks through **how to fine-tune Nemotron-3.5-ASR-Streaming to improve these languages**, including before/after results. _Link coming soon — [placeholder](#) until the blog is published._
+
 
-For more detailed information on ethical considerations for this model, please see the [Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards](Insert Link Here). <br> 
+---
 
-Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).  <br>
+## Ethical Considerations
 
-## Get Help
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
 
-### Enterprise Support
+Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+---
 
-Get access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).