Update README.md

README.md

@@ -55,7 +55,7 @@ model-index:
     metrics:
     - name: Test WER
       type: wer
-      value: 10.18
+      value: 10.19
   - task:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
@@ -68,7 +68,7 @@ model-index:
     metrics:
     - name: Test WER
       type: wer
-      value: 10.42
+      value: 10.45
   - task:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
@@ -81,7 +81,7 @@ model-index:
     metrics:
     - name: Test WER
       type: wer
-      value: 9.41
+      value: 9.43
   - task:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
@@ -95,7 +95,7 @@ model-index:
     metrics:
     - name: Test WER
       type: wer
-      value: 1.6
+      value: 1.61
   - task:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
@@ -137,7 +137,7 @@ model-index:
     metrics:
     - name: Test WER
       type: wer
-      value: 2.72
+      value: 2.71
   - task:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
@@ -173,7 +173,7 @@ img {
 # Model Overview
 
 ## Description:
-NVIDIA NeMo Canary-Qwen-2.5B is an English speech recognition model that achieves state-of-the art performance on multiple English speech benchmarks. With 2.5 billion parameters and running at 458 RTFx, Canary-Qwen-2.5B supports automatic speech-to-text recognition (ASR) in English with punctuation and capitalization (PnC). The model is intended as a transcription tool only, and not expected to extend the LLM capabilities into speech modality. This model is ready for commercial use.
+NVIDIA NeMo Canary-Qwen-2.5B is an English speech recognition model that achieves state-of-the art performance on multiple English speech benchmarks. With 2.5 billion parameters and running at 458 RTFx, Canary-Qwen-2.5B supports automatic speech-to-text recognition (ASR) in English with punctuation and capitalization (PnC). The model works in two modes: as a transcription tool (ASR mode) and as an LLM (LLM mode). In ASR mode, the model is only capable of transcribing the speech into text, but does not retain any LLM-specific skills such as reasoning. In LLM mode, the model retains all of the original LLM capabilities, which can be used to post-process the transcript, e.g. summarize it or answer questions about it. In LLM mode, the model does not "understand" the raw audio anymore - only its transcript. This model is ready for commercial use.
 
 ### License/Terms of Use: 
 Canary-Qwen-2.5B is released under the CC-BY-4.0 license. By using this model, you are agreeing to the [terms and conditions](https://choosealicense.com/licenses/cc-by-4.0/) of the license. <br>
@@ -197,8 +197,20 @@ Canary-Qwen-2.5B is released under the CC-BY-4.0 license. By using this model, y
 
 [9] [SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation](https://arxiv.org/abs/2310.09424) 
 
+### Deployment Geography:
+
+Global
+
+### Use Case:
+
+The model is intended for users requiring speech-to-text transcription capabilities for English speech, and/or transcript post-processing capabilities enabled by prompting the underlying LLMs. Typical use-cases: transcription, summarization, answering user questions about the transcript.
+
+### Release Date:
+
+Huggingface 07/15/2025 via https://huggingface.co/nvidia/canary-qwen-2.5b
+
 ## Model Architecture:
-Canary-Qwen is a Speech-Augmented Language Model (SALM) [9] model with FastConformer [2] Encoder and Transformer Decoder [3]. It is built using two base models: `nvidia/canary-1b-flash` [1,5] and `Qwen/Qwen3-1.7B` [4], a linear projection, and LoRA applied to the LLM. The audio encoder computes audio representation that is mapped to the LLM embedding space via a linear projection, and concatenated with the embeddings of text tokens. The model is prompted with "Transcribe the following: <audio>", using Qwen's chat template.
+Canary-Qwen is a Speech-Augmented Language Model (SALM) [9] model with FastConformer [2] Encoder and Transformer Decoder [3]. It is built using two base models: `nvidia/canary-1b-flash` [1,5] and `Qwen/Qwen3-1.7B` [4], a linear projection, and low-rank adaptation (LoRA) applied to the LLM. The audio encoder computes audio representation that is mapped to the LLM embedding space via a linear projection, and concatenated with the embeddings of text tokens. The model is prompted with "Transcribe the following: <audio>", using Qwen's chat template.
 
 ### Limitations
 
@@ -225,9 +237,10 @@ model = SALM.from_pretrained('nvidia/canary-qwen-2.5b')
 ```
 
 ## Input: 
+
 **Input Type(s):** Audio, text prompt <br>
-**Input Format(s):** .wav or .flac files<br>
-**Input Parameters(s):** 1D <br>
+**Input Format(s):** Audio: .wav or .flac files. Text prompt string for ASR mode: `Transcribe the following: <|audioplaceholder|>` <br>
+**Input Parameters(s):** Audio: Two-Dimensional (batch, audio-samples); Text: One-Dimensional (string) <br>
 **Other Properties Related to Input:** 16000 Hz Mono-channel Audio, Pre-Processing Not Needed <br>
 
 Input to Canary-Qwen-2.5B is a batch of prompts that include audio.
@@ -266,13 +279,14 @@ python examples/speechlm2/salm_generate.py \
 ## Output:
 **Output Type(s):** Text <br>
 **Output Format:** Text transcript as a sequence of token IDs or a string <br> 
-**Output Parameters:** 1-Dimensional text string <br>
+**Output Parameters:** One-Dimensional text string <br>
 **Other Properties Related to Output:** May Need Inverse Text Normalization <br>
 
+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.
 
 ## Software Integration:
 **Runtime Engine(s):** 
-* NeMo - 2.4.0 or higher <br>
+* NeMo - 2.5.0 or higher <br>
 
 **Supported Hardware Microarchitecture Compatibility:** <br>
 * [NVIDIA Ampere] <br>
@@ -292,11 +306,25 @@ python examples/speechlm2/salm_generate.py \
 ## Model Version(s): 
 Canary-Qwen-2.5B <br>
 
+## Training
+
+Canary-Qwen-2.5B was trained using the NVIDIA NeMo toolkit [6] for a total of 90k steps on 32 NVIDIA A100 80GB GPUs. LLM parameters were kept frozen. Speech encoder, projection, and LoRA parameters were trainable. The encoder's output frame rate is 80ms, or 12.5 tokens per second. The model was trained on approximately 1.3B tokens in total (this number inlcudes the speech encoder output frames, text response tokens, prompt tokens, and chat template tokens). 
+
+The model can be trained using this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/speechlm2/salm_train.py) and [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/speechlm2/conf/salm.yaml).
+
+The tokenizer was inherited from `Qwen/Qwen3-1.7B`.
 
 # Training and Evaluation Datasets: 
 
 ## Training Dataset:
 
+** The total size (in number of data points): approx. 40 million (speech, text) pairs
+** Total number of datasets: 26, with 18 for training and 8 for test
+** Dataset partition: Training 99.6%, testing 0.04%, validation 0%
+** Time period for training data collection: 1990-2025
+** Time period for testing data collection: 2005-2022
+** Time period for validation data collection N/A (unused)
+
 The Canary-Qwen-2.5B model is trained on a total of 234K hrs of publicly available speech data.
 The datasets below include conversations, videos from the web and audiobook recordings.
 
@@ -306,9 +334,11 @@ The datasets below include conversations, videos from the web and audiobook reco
 **Labeling Method:**
 * Hybrid: Human, Automated <br>
 
+### Properties
+
 #### English (234.5k hours)
 
-The majority of the training data come from English portion of the Granary dataset [7]:
+The majority of the training data comes from the English portion of the Granary dataset [7]:
 
 - YouTube-Commons (YTC) (109.5k hours)
 - YODAS2 (77k hours)
@@ -355,20 +385,6 @@ Noise Robustness:
 Model Fairness:
 * [Casual Conversations Dataset](https://arxiv.org/pdf/2104.02821)
 
-## Training
-
-Canary-Qwen-2.5B was trained using the NVIDIA NeMo toolkit [6] for a total of 90k steps on 32 NVIDIA A100 80GB GPUs. LLM parameters were kept frozen. Speech encoder, projection, and LoRA parameters were trainable. The encoder's output frame rate is 80ms, or 12.5 tokens per second. The model was trained on approximately 1.3B tokens in total (this number inlcudes the speech encoder output frames, text response tokens, prompt tokens, and chat template tokens). The model was trained in bfloat16 precision (not using AMP) and bucketing. 
-
-The model can be trained using this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/speechlm2/salm_train.py) and [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/speechlm2/conf/salm.yaml).
-
-The tokenizer was inherited from `Qwen/Qwen3-1.7B`.
-
-## Inference:
-**Engine:** NVIDIA NeMo <br>
-**Test Hardware :** <br>
-* A6000 <br>
-* A100 <br>
-
 ## Performance
 
 The ASR predictions were generated using greedy decoding.
@@ -381,7 +397,7 @@ WER on [HuggingFace OpenASR leaderboard](https://huggingface.co/spaces/hf-audio/
 
 | **Version** | **Model**     | **RTFx**   | **Mean**   | **AMI**   | **GigaSpeech**   | **LS Clean**   | **LS Other**   | **Earnings22**   | **SPGISpech**   | **Tedlium**   | **Voxpopuli**   |
 |:---------:|:-----------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
-| 2.4.0  | Canary-Qwen-2.5B | 458.5 | 5.62 | 10.18 | 9.41 | 1.60 | 3.10 | 10.42 | 1.90 | 2.72 | 5.66 |
+| 2.5.0  | Canary-Qwen-2.5B | 458.5 | 5.62 | 10.18 | 9.41 | 1.60 | 3.10 | 10.42 | 1.90 | 2.72 | 5.66 |
 
 More details on evaluation can be found at [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)
 
@@ -389,14 +405,14 @@ More details on evaluation can be found at [HuggingFace ASR Leaderboard](https:/
 Number of characters per minute on [MUSAN](https://www.openslr.org/17) 48 hrs eval set (`max_new_tokens=50` following `nvidia/canary-1b-flash` evaluation)
 | **Version** | **Model** | **# of character per minute** |
 |:-----------:|:---------:|:----------:|
-| 2.4.0       | Canary-Qwen-2.5B |   138.1   |
+| 2.5.0       | Canary-Qwen-2.5B |   138.1   |
 
 ### Noise Robustness
 WER on [Librispeech Test Clean](https://www.openslr.org/12) at different SNR (signal to noise ratio) levels of additive white noise
 
 | **Version** | **Model** | **SNR 10** | **SNR 5** | **SNR 0** | **SNR -5** |
 |:-----------:|:---------:|:----------:|:----------:|:----------:|:----------:|
-| 2.4.0       | Canary-Qwen-2.5B |    2.41%   |   4.08%   |   9.83%   |    30.60%  |
+| 2.5.0       | Canary-Qwen-2.5B |    2.41%   |   4.08%   |   9.83%   |    30.60%  |
 
 ## Model Fairness Evaluation
 
@@ -418,6 +434,13 @@ As outlined in the paper "Towards Measuring Fairness in AI: the Casual Conversat
 
 (Error rates for fairness evaluation are determined by normalizing both the reference and predicted text, similar to the methods used in the evaluations found at https://github.com/huggingface/open_asr_leaderboard.)
 
+## Inference:
+**Engine:** NVIDIA NeMo <br>
+**Test Hardware :** <br>
+* A6000 <br>
+* A100 <br>
+* RTX 5090 <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.  
-For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).