Updated files per bugs 5970369, 5966307, and 5966925

docs/docs/extraction/audio.md

@@ -1,8 +1,8 @@
 # Extract Speech with NeMo Retriever Library
 
 This documentation describes two methods to run [NeMo Retriever Library](overview.md) 
-with the [RIVA ASR NIM microservice](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html) 
-to extract speech from audio files.
+with the [parakeet-1-1b-ctc-en-us ASR NIM microservice](https://docs.nvidia.com/nim/speech/latest/asr/deploy-asr-models/parakeet-ctc-en-us.html) 
+(`nvcr.io/nim/nvidia/parakeet-1-1b-ctc-en-us`) to extract speech from audio files.
 
 - Run the NIM locally by using Docker Compose
 - Use NVIDIA Cloud Functions (NVCF) endpoints for cloud-based inference
@@ -22,12 +22,12 @@ Currently, you can extract speech from the following file types:
 
 [NeMo Retriever Library](overview.md) supports extracting speech from audio files for Retrieval Augmented Generation (RAG) applications. 
 Similar to how the multimodal document extraction pipeline leverages object detection and image OCR microservices, 
-NeMo Retriever leverages the [RIVA ASR NIM microservice](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html) 
+NeMo Retriever leverages the [parakeet-1-1b-ctc-en-us ASR NIM microservice](https://docs.nvidia.com/nim/speech/latest/asr/deploy-asr-models/parakeet-ctc-en-us.html) 
 to transcribe speech to text, which is then embedded by using the NeMo Retriever embedding NIM. 
 
 !!! important
 
-    Due to limitations in available VRAM controls in the current release, the RIVA ASR NIM microservice must run on a [dedicated additional GPU](support-matrix.md). For the full list of requirements, refer to [Support Matrix](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/support-matrix.html).
+    Due to limitations in available VRAM controls in the current release, the parakeet-1-1b-ctc-en-us ASR NIM microservice must run on a [dedicated additional GPU](support-matrix.md). For the full list of requirements, refer to [Support Matrix](support-matrix.md).
 
 This pipeline enables users to retrieve speech files at the segment level.
 
@@ -43,7 +43,7 @@ Use the following procedure to run the NIM locally.
 
 !!! important
 
-    The RIVA ASR NIM microservice must run on a [dedicated additional GPU](support-matrix.md). Edit docker-compose.yaml to set the device_id to a dedicated GPU: device_ids: ["1"] or higher.
+    The parakeet-1-1b-ctc-en-us ASR NIM microservice must run on a [dedicated additional GPU](support-matrix.md). Edit docker-compose.yaml to set the device_id to a dedicated GPU: device_ids: ["1"] or higher.
 
 1. To access the required container images, log in to the NVIDIA Container Registry (nvcr.io). Use [your NGC key](ngc-api-key.md) as the password. Run the following command in your terminal.
 

docs/docs/extraction/nv-ingest-python-api.md

@@ -571,5 +571,5 @@ results = ingestor.ingest()
 - [Split Documents](chunking.md)
 - [Troubleshoot Nemo Retriever Extraction](troubleshoot.md)
 - [Advanced Visual Parsing](nemoretriever-parse.md)
-- [Use NeMo Retriever Library with Riva for Audio Processing](audio.md)
+- [Use NeMo Retriever Library with the Parakeet ASR NIM for Audio Processing](audio.md)
 - [Use Multimodal Embedding](vlm-embed.md)

docs/docs/extraction/python-api-reference.md

@@ -663,5 +663,5 @@ results = ingestor.ingest()
 - [Split Documents](chunking.md)
 - [Troubleshoot NeMo Retriever Library](troubleshoot.md)
 - [Advanced Visual Parsing](nemoretriever-parse.md)
-- [Use the NeMo Retriever Library with Riva for Audio Processing](audio.md)
+- [Use the NeMo Retriever Library with the Parakeet ASR NIM for Audio Processing](audio.md)
 - [Use Multimodal Embedding](vlm-embed.md)

docs/docs/extraction/quickstart-guide.md

@@ -393,7 +393,7 @@ You can specify multiple `--profile` options.
 | Profile               | Type     | Description                                                       | 
 |-----------------------|----------|-------------------------------------------------------------------| 
 | `retrieval`           | Core     | Enables the embedding NIM and (optional) GPU-accelerated Milvus. Omit this profile to use the default LanceDB backend.           | 
-| `audio`               | Advanced | Use [Riva](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html) for processing audio files. For more information, refer to [Audio Processing](audio.md). | 
+| `audio`               | Advanced | Use the [parakeet-1-1b-ctc-en-us](https://docs.nvidia.com/nim/speech/latest/asr/deploy-asr-models/parakeet-ctc-en-us.html) ASR NIM (`nvcr.io/nim/nvidia/parakeet-1-1b-ctc-en-us`) for processing audio files. For more information, refer to [Audio Processing](audio.md). | 
 | `nemotron-parse`      | Advanced | Use [nemotron-parse](https://build.nvidia.com/nvidia/nemotron-parse), which adds state-of-the-art text and table extraction. For more information, refer to [Advanced Visual Parsing](nemoretriever-parse.md). | 
 | `vlm`                 | Advanced | Use [llama 3.1 Nemotron 8B Vision](https://build.nvidia.com/nvidia/llama-3.1-nemotron-nano-vl-8b-v1/modelcard) for image captioning of unstructured images and infographics. This profile enables the `caption` method in the Python API to generate text descriptions of visual content. For more information, refer to [Use Multimodal Embedding](vlm-embed.md) and [Extract Captions from Images](nv-ingest-python-api.md#extract-captions-from-images). | 
 

docs/docs/extraction/support-matrix.md

@@ -22,7 +22,7 @@ The core pipeline features include the following:
 Advanced features require additional GPU support and disk space. 
 This includes the following:
 
-- Audio extraction — Use [Riva](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html) for processing audio files. For more information, refer to [Audio Processing](audio.md).
+- Audio extraction - parakeet-1-1b-ctc-en-us — Use the [Parakeet CTC English (en-US) ASR NIM](https://docs.nvidia.com/nim/speech/latest/asr/deploy-asr-models/parakeet-ctc-en-us.html) (`nvcr.io/nim/nvidia/parakeet-1-1b-ctc-en-us`) for processing audio files. For more information, refer to [Audio Processing](audio.md).
 - Advanced visual parsing — Use [nemotron-parse](https://docs.nvidia.com/nim/vision-language-models/latest/examples/nemotron-parse/overview.html), which adds state-of-the-art text and table extraction. For more information, refer to [Advanced Visual Parsing ](nemoretriever-parse.md).
 - VLM — Use [nemotron-nano-12b-v2-vl](https://build.nvidia.com/nvidia/nemotron-nano-12b-v2-vl/modelcard) for experimental image captioning of unstructured images. 
 
@@ -55,8 +55,8 @@ The following are the hardware requirements to run NeMo Retriever Library.
 | GPU            | Memory                    | 96GB          | 180GB         | 141GB         | 80GB        | 80GB        | 40GB          | 24GB          | 48GB   | 32GB GDDR7 (GB203)     |
 | Core Features  | Total GPUs                | 1             | 1             | 1             | 1           | 1           | 1             | 1             | 1      | 1                      |
 | Core Features  | Total Disk Space          | ~150GB        | ~150GB        | ~150GB        | ~150GB      | ~150GB      | ~150GB        | ~150GB        | ~150GB | ~150GB                 |
-| Audio          | Additional Dedicated GPUs | 1             | 1             | 1             | 1           | 1           | 1             | 1             | 1      | 1¹                     |
-| Audio          | Additional Disk Space     | ~37GB         | ~37GB         | ~37GB         | ~37GB       | ~37GB       | ~37GB         | ~37GB         | ~37GB  | ~37GB¹                 |
+| Audio (parakeet-1-1b-ctc-en-us) | Additional Dedicated GPUs | 1             | 1             | 1             | 1           | 1           | 1             | 1             | 1      | 1¹                     |
+| Audio (parakeet-1-1b-ctc-en-us) | Additional Disk Space     | ~37GB         | ~37GB         | ~37GB         | ~37GB       | ~37GB       | ~37GB         | ~37GB         | ~37GB  | ~37GB¹                 |
 | nemotron-parse | Additional Dedicated GPUs | Not supported | Not supported | Not supported | 1           | 1           | 1             | 1             | 1      | Not supported²         |
 | nemotron-parse | Additional Disk Space     | Not supported | Not supported | Not supported | ~16GB       | ~16GB       | ~16GB         | ~16GB         | ~16GB  | Not supported²         |
 | VLM            | Additional Dedicated GPUs | 1             | 1             | 1             | 1           | 1           | Not supported | Not supported | 1      | Not supported³         |
@@ -73,4 +73,4 @@ and run only the embedder, reranker, and your vector database.
 - [Prerequisites](prerequisites.md)
 - [Release Notes](releasenotes-nv-ingest.md)
 - [NVIDIA NIM for Vision Language Models Support Matrix](https://docs.nvidia.com/nim/vision-language-models/latest/support-matrix.html)
-- [NVIDIA Riva Support Matrix](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/support-matrix/support-matrix.html)
+- [NVIDIA Speech NIM Microservices](https://docs.nvidia.com/nim/speech/latest/reference/support-matrix/index.html)

docs/docs/extraction/troubleshoot.md

@@ -40,7 +40,7 @@ Before you change the `-u` setting, consider the following:
 - For `-u` we recommend 10,000 as a baseline, but you might need to raise or lower it based on your actual usage and system configuration.
 
 ```bash
-ulimit -u 10,000
+ulimit -u 10000
 ```
 
 
@@ -89,7 +89,7 @@ Before you change the `-n` setting, consider the following:
 - For `-n` we recommend 10,000 as a baseline, but you might need to raise or lower it based on your actual usage and system configuration.
 
 ```bash
-ulimit -n 10,000
+ulimit -n 10000
 ```