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Model Overview

Modular SpeechLLM [1] is a model that combines a pretrained audio encoder with a pretrained large language model (LLM) so that the LLM model can perform speech-to-text tasks and answer questions based on the input audios. The model is trained on several tasks, including ASR, AST, SpeechQA and AudioQA, with a total of about 32K hours of audios.

Model Architecture

There are three main components of a modular SpeechLLM model:

An audio encoder that processes the input audio and produces a sequence of audio embeddings.
A modality adapter that processes the audio embeddings and produces a sequence of embeddings in the same latent space as the token embeddings of a pretrained large language model (LLM).
A pretrained large language model (LLM) that processes embeddings from the modality adapter as well as token embeddings of input prompt, and produces the text output. The audio embeddings and text token embeddings are concatenated in time dimension before going into the LLM.

Specifically, we use a 17-layer FastConformer [2] as the audio encoder, a 2-layer FastConformer as modality adapter, and Llama-2-7b-chat [3] as the pretrained LLM and add LoRA [4] to it. We freeze the original LLM parameters, while tuning everything else. The total number of parameters is around 7B, while trainable params are about 122M.

Training

The model is implemented with NVIDIA NeMo toolkit [5], and can be trained with this example script and this base config.

Datasets

The model is trained on the following datasets:

NeMo ASRSet 3.0: English only
MuST-C: En->De, En->Ja, En->Zh
MS-MACRO: English only
DynamicSuperb

Performance

All results are obtained with greedy decoding.

Speech-to-Text Recognition (ASR)

The ASR performance is evaluated by word error rate (WER %):

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Version	MCV-7.1-test	Librispeech-test-other	WSJ-eval
1.23.1	8.53	4.65	2.07

Speech-to-Text Translation (AST)

AST performance is evaluated by BLEU score on FLEURS dataset. It should be noted that the model was not trained on paired data of En->Es or En->Fr, but still it's able to perform zero-shot AST with decent performance.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Version	En->De	En->Es	En->Fr
1.23.1	27.41	16.97	25.79

SpeechQA

SpeechQA performance is evaluated with ROUGE scores on the MS-MACRO test set.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Version	ROUGE-1	ROUGE-2	ROUGE-L
1.23.1	64.79	50.41	63.14

Multi-task Audio Understanding

We evaluate on the six representative tasks in DynamicSUPERB leaderboard, using accuracy (%) as metric.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Version	Audio	Content	Degradation	Paralinguistics	Semantics	Speaker
1.23.1	9.0	92.50	79.50	28.00	66.00	65.50

How to Use this Model

Input Format

You'll need to prepare data in the NeMo manifest format, where each line is a python dictionary with some keys, for example:

{\n    \"audio_filepath\": \"path/to/audio.wav\",\n    \"offset\": 0.0, # offset of the audio in seconds, this is an optional field\n    \"duration\": 10.0 , # duration of the audio in seconds, can set to `None` to load the whole audio\n    \"context\": \"what is the transcription of the audio?\", # text prompt for the audio, see below for more details\n    \"answer\": \"the transcription of the audio\", # optional for inference, default to \"na\" in dataloader\n}\n

Inference with SpeechLLM

The script you need to perform inference is modular_audio_gpt_eval.py, and the corresponding config file is modular_audio_gpt_config_eval.yaml.

If you want to load a pretrained SpeechLLM from cloud, you can use the following script:

TEST_MANIFESTS=\"[/data/test_1.json,/data/test_2.json]\"\nTEST_NAMES=\"[test-1,test-2]\"\nCUDA_VISIBLE_DEVICES=0 python modular_audio_gpt_eval.py \\\n    model.from_pretrained=\"speechllm_fc_llama2_7b\" \\\n    model.data.test_ds.manifest_filepath=$TEST_MANIFESTS \\\n    model.data.test_ds.names=$TEST_NAMES \\\n    model.data.test_ds.global_batch_size=8 \\\n\tmodel.data.test_ds.micro_batch_size=8 \\\n\tmodel.data.test_ds.tokens_to_generate=256 \\\n    ++inference.greedy=False \\\n    ++inference.top_k=50 \\\n    ++inference.top_p=0.95 \\\n    ++inference.temperature=0.4 \\\n    ++inference.repetition_penalty=1.2 \\\n    ++model.data.test_ds.output_dir=\"./test_outputs\"\n

If you have a local .nemo file, you can use model.restore_from_path=/path/to/model.nemo to replace the line model.from_pretrained=\"speechllm_fc_llama2_7b\" in the above example.

Input

The model takes single-channel audios of 16000 Hz, as well as text prompts as input.

Output

The model produces natural language text output.

Limitations

Although the model has some zero-shot extension capabilities, it works best on the languages and tasks that it's trained on, and might not work well on unseen languages or tasks.

References

[1] SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation

[2] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition

[3] Llama-2-7b-chat

[4] LoRA: Low-Rank Adaptation of Large Language Models

[5] NVIDIA NeMo Toolkit

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The model is trained on several tasks, including ASR, AST, SpeechQA and AudioQA, with a total of about 32K hours of audios.\n\n## Model Architecture\n\nThere are three main components of a modular SpeechLLM model:\n- An audio encoder that processes the input audio and produces a sequence of audio embeddings.\n- A modality adapter that processes the audio embeddings and produces a sequence of embeddings in the same latent space as the token embeddings of a pretrained large language model (LLM).\n- A pretrained large language model (LLM) that processes embeddings from the modality adapter as well as token embeddings of input prompt, and produces the text output. The audio embeddings and text token embeddings are concatenated in time dimension before going into the LLM.\n\nSpecifically, we use a 17-layer FastConformer [2] as the audio encoder, a 2-layer FastConformer as modality adapter, and Llama-2-7b-chat [3] as the pretrained LLM and add LoRA [4] to it. We freeze the original LLM parameters, while tuning everything else. The total number of parameters is around 7B, while trainable params are about 122M.\n\n## Training\n\nThe model is implemented with NVIDIA NeMo toolkit [5], and can be trained with this [example script](https://github.com/NVIDIA/NeMo/blob/modular_speechllm/examples/multimodal/speechllm/modular_audio_gpt_train.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/modular_speechllm/examples/multimodal/speechllm/conf/modular_audio_gpt_config_peft.yaml).\n\n### Datasets\n\nThe model is trained on the following datasets:\n- [NeMo ASRSet 3.0](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_transducer_large): English only\n- [MuST-C](https://aclanthology.org/N19-1202/): En->De, En->Ja, En->Zh\n- [MS-MACRO](https://huggingface.co/datasets/ms_marco): English only\n- [DynamicSuperb](https://arxiv.org/abs/2309.09510)\n\n## Performance\n\nAll results are obtained with greedy decoding.\n\n### Speech-to-Text Recognition (ASR)\n\nThe ASR performance is evaluated by word error rate (WER %):\n\n| Version | MCV-7.1-test | Librispeech-test-other | WSJ-eval |\n|:-------:|:------------:|:----------------------:|:--------:|\n| 1.23.1 | 8.53 | 4.65 | 2.07 |\n\n\n### Speech-to-Text Translation (AST)\n\nAST performance is evaluated by BLEU score on [FLEURS](https://huggingface.co/datasets/google/fleurs) dataset. It should be noted that the model was not trained on paired data of En->Es or En->Fr, but still it's able to perform zero-shot AST with decent performance.\n\n| Version | En->De | En->Es | En->Fr |\n|:-------:|:------:|:------:|:------:|\n| 1.23.1 | 27.41 | 16.97 | 25.79 |\n\n\n\n### SpeechQA\n\nSpeechQA performance is evaluated with ROUGE scores on the MS-MACRO test set.\n\n| Version | ROUGE-1 | ROUGE-2 | ROUGE-L |\n|:-------:|:-------:|:-------:|:-------:|\n| 1.23.1 | 64.79 | 50.41 | 63.14 |\n\n### Multi-task Audio Understanding\n\nWe evaluate on the six representative tasks in DynamicSUPERB [leaderboard](https://github.com/cyhuang-tw/dlhlp-dynamic-superb-leaderboard/blob/main/leaderboard.md), using accuracy (%) as metric.\n\n| Version | Audio | Content | Degradation | Paralinguistics | Semantics | Speaker |\n|:-------:|:-----:|:-------:|:-----------:|:---------------:|:---------:|:-------:|\n| 1.23.1 | 9.0 | 92.50 | 79.50 | 28.00 | 66.00 | 65.50 |\n\n\n## How to Use this Model\n\n\n### Input Format\n\nYou'll need to prepare data in the NeMo manifest format, where each line is a python dictionary with some keys, for example:\n```\n{\n \"audio_filepath\": \"path/to/audio.wav\",\n \"offset\": 0.0, # offset of the audio in seconds, this is an optional field\n \"duration\": 10.0 , # duration of the audio in seconds, can set to `None` to load the whole audio\n \"context\": \"what is the transcription of the audio?\", # text prompt for the audio, see below for more details\n \"answer\": \"the transcription of the audio\", # optional for inference, default to \"na\" in dataloader\n}\n```\n\n### Inference with SpeechLLM\n\nThe script you need to perform inference is [modular_audio_gpt_eval.py](https://github.com/NVIDIA/NeMo/blob/main/examples/multimodal/speech_llm/modular_audio_gpt_eval.py), and the corresponding config file is [modular_audio_gpt_config_eval.yaml](https://github.com/NVIDIA/NeMo/blob/main/examples/multimodal/speech_llm/conf/modular_audio_gpt_config_eval.yaml).\n\nIf you want to load a pretrained SpeechLLM from cloud, you can use the following script:\n\n```bash\nTEST_MANIFESTS=\"[/data/test_1.json,/data/test_2.json]\"\nTEST_NAMES=\"[test-1,test-2]\"\nCUDA_VISIBLE_DEVICES=0 python modular_audio_gpt_eval.py \\\n model.from_pretrained=\"speechllm_fc_llama2_7b\" \\\n model.data.test_ds.manifest_filepath=$TEST_MANIFESTS \\\n model.data.test_ds.names=$TEST_NAMES \\\n model.data.test_ds.global_batch_size=8 \\\n\tmodel.data.test_ds.micro_batch_size=8 \\\n\tmodel.data.test_ds.tokens_to_generate=256 \\\n ++inference.greedy=False \\\n ++inference.top_k=50 \\\n ++inference.top_p=0.95 \\\n ++inference.temperature=0.4 \\\n ++inference.repetition_penalty=1.2 \\\n ++model.data.test_ds.output_dir=\"./test_outputs\"\n```\n\nIf you have a local `.nemo` file, you can use `model.restore_from_path=/path/to/model.nemo` to replace the line `model.from_pretrained=\"speechllm_fc_llama2_7b\"` in the above example.\n\n\n### Input\n\nThe model takes single-channel audios of 16000 Hz, as well as text prompts as input.\n\n\n### Output\n\nThe model produces natural language text output.\n\n\n## Limitations\n\nAlthough the model has some zero-shot extension capabilities, it works best on the languages and tasks that it's trained on, and might not work well on unseen languages or tasks.\n\n## References\n\n[1] [SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation](https://arxiv.org/abs/2310.09424)\n\n[2] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)\n\n[3] [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)\n\n[4] [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685)\n\n[5] [NVIDIA NeMo 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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1245":1246,"_1247":1248,"_1249":1250,"_1251":1252},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[355,356,357,358,359],{"_360":897,"_77":898,"_164":899,"_364":85,"_365":16,"_367":900,"_369":901},{"_360":686,"_77":687,"_164":688,"_364":30,"_365":689,"_367":690,"_369":691},{"_360":586,"_77":587,"_164":588,"_364":30,"_365":58,"_367":589,"_369":590},{"_360":371,"_77":372,"_164":373,"_364":30,"_365":374,"_367":375,"_369":376},{"_360":361,"_77":362,"_164":363,"_364":30,"_365":366,"_367":368,"_369":370},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538],[377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393],{"_394":500,"_204":500,"_164":165,"_367":501},{"_394":495,"_204":495,"_164":165,"_367":496},{"_394":491,"_204":491,"_164":165,"_367":492},{"_394":486,"_204":486,"_164":165,"_367":487},{"_394":481,"_204":481,"_164":165,"_367":482},{"_394":469,"_204":469,"_164":165,"_367":470},{"_394":457,"_204":457,"_164":165,"_367":458},{"_394":452,"_204":452,"_164":165,"_367":453},{"_394":441,"_204":441,"_164":165,"_367":442},{"_394":436,"_204":436,"_164":165,"_367":437},{"_394":431,"_204":431,"_164":165,"_367":432},{"_394":426,"_204":426,"_164":165,"_367":427},{"_394":421,"_204":421,"_164":165,"_367":422},{"_394":416,"_204":416,"_164":165,"_367":417},{"_394":411,"_204":411,"_164":165,"_367":412},{"_394":404,"_204":404,"_164":165,"_367":405},{"_394":395,"_204":395,"_164":165,"_367":396},"hostName","amazon-adsystem.com",[397,398],{"_77":403,"_400":395,"_401":30,"_164":402},{"_77":399,"_400":395,"_401":30,"_164":402},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[406,407],{"_77":410,"_400":404,"_401":30,"_164":409},{"_77":408,"_400":404,"_401":30,"_164":409},"IDE","This domain is owned by Doubleclick (Google). 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It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[459,460,461,462],{"_77":468,"_400":457,"_401":30,"_164":464},{"_77":466,"_400":457,"_401":30,"_164":467},{"_77":465,"_400":457,"_401":30,"_164":464},{"_77":463,"_400":457,"_401":30,"_164":464},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646],[591,592,593,594,595],{"_394":486,"_204":486,"_164":165,"_367":617},{"_394":452,"_204":452,"_164":165,"_367":612},{"_394":605,"_204":605,"_164":165,"_367":606},{"_394":601,"_204":601,"_164":165,"_367":602},{"_394":596,"_204":596,"_164":165,"_367":597},"nvcrm.my.site.com",[598],{"_77":599,"_400":596,"_401":30,"_164":600},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[603],{"_77":604,"_400":601,"_401":30,"_164":435},"edge","github.com",[607,608],{"_77":611,"_400":605,"_401":30,"_164":610},{"_77":609,"_400":605,"_401":30,"_164":610},"logged_in","This domain is owned by Github. 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The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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