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  2. LICENSE +41 -0
  3. README.md +171 -0
  4. config.json +279 -0
  5. generation_config.json +4 -0
  6. model.safetensors +3 -0
  7. special_tokens_map.json +47 -0
  8. tokenizer.json +3 -0
  9. tokenizer_config.json +2102 -0
.gitattributes CHANGED
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LICENSE ADDED
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+ Llama-SpeechLMM License Agreement
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+ 1. Grant of License. These software, models and algorithms (collectively, the "Software") is licensed, not sold, and is provided strictly for academic, research, and educational purposes only. By using the Software, you agree to be bound by the terms of this agreement.
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+ 2. Ownership and Intellectual Property Rights. The Software is the exclusive property of the Meetween Consortium (the “Consortium”), Meetween is funded by the European Union’s Horizon Europe Framework under Grant Agreement No. 101135798, or its assignors. All intellectual property rights, including copyrights, patents, trademarks, trade secrets, and any other proprietary rights, are fully reserved and protected to the maximum extent permitted by law. No rights are granted except as expressly provided in this Agreement. You may not, nor may you permit any third party to, attempt to register a trademark, obtain a copyright, patent, design right, or any other intellectual property rights in or to the Software, any part thereof, or any derivative work based on the Software. Any unauthorized attempt to claim ownership of intellectual property derived from or incorporating the Software is strictly prohibited.
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+ The Software may incorporate proprietary elements, including but not limited to unique structures, designs, algorithms, and code, whether registered or unregistered (the "Intellectual Property"). The Consortium and its assignors are the sole owner of said Intellectual Property. You may not use any Intellectual Property in connection with any business, product, or service, or in any manner that may imply endorsement of any business, message, product, or service or that is likely to cause confusion with or dilute, blur, or tarnish such Intellectual Property or the goodwill or ownership thereof. You may not reproduce, modify, distribute, or otherwise exploit the Intellectual Property for any purpose beyond those explicitly permitted by this Agreement.
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+ 3. Permitted Uses. The Software may only be used for non-commercial academic research, educational purposes, and personal study. Any other use is expressly forbidden. You may not, nor may you permit any third party to, use the Software or any part thereof to advertise, market, promote, or sell any product or service. You may not use the Software in any production environment, nor may you deploy it in a way that results in financial gain, whether direct or indirect. The Software may not be used in connection with any governmental, military, intelligence, surveillance, or law enforcement activities. Any use of the Software that is not explicitly authorized in this Agreement is prohibited. You may not attempt to create derivative works of the Software, nor claim or register any intellectual property rights based on the Software or any modifications thereof.
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+ 4. Prohibited Uses. You may not modify, adapt, reverse engineer, decompile, disassemble, or create derivative works of the Software in any way, including, without limitation, altering its structure, design, functionality, or attributes, except for cases provided by mandatory provisions of law. You may not distribute, sublicense, lease, rent, or sell the Software, nor may you use it for consulting or any revenue-generating activities. The Software may not be separated, unlinked, or decoupled from any associated intellectual property. You may not attempt to acquire any additional intellectual property rights in or related to the Software. Any use of the Software that violates applicable laws or regulations is strictly forbidden.
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+ * Modifies the Intellectual Property in any way, including, without limitation, the alteration of structures, designs, algorithms, or attributes.
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+ * Uses the Intellectual Property to advertise, market, promote, or sell any product or service.
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+ * Attempts to register any trademarks, copyrights, patents, or other proprietary rights related to the Software or any portion thereof.
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+ * Includes any content that is obscene, vulgar, defamatory, offensive, or that promotes illegal activities.
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+ This contract does not exclude or limit uses permitted by mandatory provisions of law.
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+ 5. No Warranty and Limitation of Liability. THE SOFTWARE IS PROVIDED "AS IS," WITHOUT ANY EXPRESS OR IMPLIED WARRANTY, INCLUDING BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR NON-INFRINGEMENT, TO THE MAXIMUM EXTENT PERMITTED BY LAW.
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+ TO THE MAXIMUM EXTENT PERMITTED BY LAW, YOU ACKNOWLEDGE THAT THE CONSORTIUM SHALL NOT BE LIABLE FOR ANY INDIRECT, INCIDENTAL, CONSEQUENTIAL, OR PUNITIVE DAMAGES, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES, ARISING FROM OR RELATED TO YOUR USE OR MISUSE OF THE SOFTWARE.
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+ THIS CLAUSE DOES NOT EXCLUDE OR LIMIT THE CONSORTIUM’S LIABILITY FOR DAMAGES RESULTING FROM WILLFUL MISCONDUCT OR GROSS NEGLIGENCE, OR IN CASES WHERE LIABILITY EXCLUSION IS PROHIBITED BY APPLICABLE LAW.
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+ 6. Indemnification. You agree to indemnify and hold harmless the Consortium, its members, contributors, affiliates, and licensors from any claims, liabilities, or damages arising from your use of the Software in violation of this Agreement. You further agree to defend the Consortium against any claim asserting that your use of the Software has violated any law, regulation, or third-party right, and to reimburse the Consortium for any expenses incurred in connection with such claims.
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+ 7. Termination. This Agreement shall commence upon the User's acceptance or the beginning of the use of the Software and shall remain in full force and effect until terminated in accordance with the terms and conditions herein. This license will be terminated automatically if you fail to comply with any of the terms set forth in this Agreement. Upon termination, you must cease all use of the Software and destroy all copies in your possession. The Consortium reserves the right to pursue legal action for any unauthorized use of the Software.
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+
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+ 8. Governing Law. This Agreement shall be governed by and construed in accordance with Italian law. The competent jurisdiction for any dispute arising from this Agreement shall be the Court of Rome.
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+ 9. Severability. The invalidity, in whole or in part, of any clause of this Agreement shall not affect the validity of the remainder of the Agreement and the Parties shall replace such invalid provisions with valid provisions reflecting as closely as possible the intent underlying the invalid provisions.
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+ 10. Contact Information. For any inquiries regarding this license, please contact [email protected]. By downloading, accessing, or using the Software, you acknowledge that you have read, understood, and agreed to be bound by these terms.
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+
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+ 11. LLAMA license. Built with Llama. Additional information: LLAMA 3.1 COMMUNITY LICENSE AGREEMENT (https://www.llama.com/llama3_1/license/).
README.md ADDED
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+ ---
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+ library_name: transformers
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: Llama-speechlmm-1.0-s
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+ results: []
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+ ---
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+
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+ ## Model information
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+
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+ The SpeechLMM 1.0 collection of multimodal and multilingual large language models is a collection of instruction-tuned generative models in 4 different sizes: S (2B), M (4B), L (9B) and XL (71B), supporting text, audio and video as input and only text as output. The SpeechLMM 1.0 models are optimized for various X-to-text generation tasks, namely:
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+
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+ - Text-to-text Instruction Following
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+ - Machine Translation
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+ - Text Summarization
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+ - Automatic Speech Recognition
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+ - Speech Translation
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+ - Speech Summarization
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+ - Spoken Question Answering
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+ - Spoken Language Understanding
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+ - Visual Speech Recognition
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+
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+ **Model Developer:** Meetween consortium
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+
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+ **Supported Languages:** English, French, Italian, German, and Spanish are officially supported (for a subset of the supported tasks). The Llama 3.X backbone and the SeamlessM4T v2 audio encoder have been trained on a broader collection of languages than these 5 supported languages, so the model might exhibit good performance on other languages too.
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+
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+ **Model Release Date:** Feb 28, 2025
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+
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+ **License:** see [LICENSE](LICENSE)
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+
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+ ### Model Architecture
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+
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+ SpeechLMM 1.0 an auto-regressive multimodal language model based on a Llama 3.X backbone (X varies with the model size), a speech-specific stack consisting of a pre-trained audio encoder ([SeamlessM4T v2](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/)) and an audio adapter, and a video-specific stack consisting of a pre-trained video encoder ([Auto-AVSR](https://ieeexplore.ieee.org/document/10096889)) and a video adapter.
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+
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+ <!-- TODO: add the image of the model architecture here -->
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+
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+ | Model | Params | Input modalities | Output modalities | Context Length |
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+ |:---------------- |:----------- |:------------------------------------------ |:----------------- |:-------------- |
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+ | SpeechLMM 1.0 S | 2B (2.17B) | Multilingual text and audio, English video | Multilingual Text | 128k |
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+ | SpeechLMM 1.0 M | 4B (4.15B) | Multilingual text and audio, English video | Multilingual Text | 128k |
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+ | SpeechLMM 1.0 L | 9B (8.98B) | Multilingual text and audio, English video | Multilingual Text | 128k |
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+ | SpeechLMM 1.0 XL | 71B (71.5B) | Multilingual text and audio, English video | Multilingual Text | 128k |
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+
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+ #### Audio and video encoders
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+
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+ For all the 4 sizes of SpeechLMM 1.0, the audio encoder is **SeamlessM4T v2 Large** (`facebook/seamless-m4t-v2-large`) and the video encoder is **Auto-AVSR** (`vsr_trlrs3vox2_base`).
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+
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+ #### Audio and video adapters
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+
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+ For all the 4 sizes of SpeechLMM 1.0, the audio and video adapters are:
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+ | Modality | Architecture | Number of layers | Compression factor |
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+ | :------- | :----------- | :--------------- | :----------------- |
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+ | Audio | MLP | 4 | 1 |
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+ | Video | Window-level Q-former <br> (4 queries) | 4 | 4 |
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+
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+ #### LLM backbone
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+
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+ | Model | Backbone |
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+ |:---------------- |:---------------------- |
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+ | SpeechLMM 1.0 S | Llama 3.2 1B Instruct |
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+ | SpeechLMM 1.0 M | Llama 3.2 3B Instruct |
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+ | SpeechLMM 1.0 L | Llama 3.1 8B Instruct |
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+ | SpeechLMM 1.0 XL | Llama 3.3 70B Instruct |
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+
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+ ## Evaluation results
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+
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+ ### ASR Metrics
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+
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+ | testset | language | wer |
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+ |:----------|:-----------|------:|
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+ | MUSTC | en | 19.2 |
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+ | MTEDX | it | 29.43 |
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+ | MTEDX | fr | 28.97 |
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+ | ACL6060 | en | 19.4 |
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+ | MTEDX | es | 29.71 |
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+
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+ ### SQA Metrics
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+
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+ | testset | language | accuracy | input_questions | evaluated_questions |
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+ |:--------------|:-----------|-----------:|------------------:|----------------------:|
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+ | SpeechMassive | en | 65.93 | 5351 | 5193 |
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+
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+ ### MT Metrics
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+
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+ | testset | source-language | target-language | bleu | chrf |
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+ |:----------|:------------------|:------------------|-------:|-------:|
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+ | FLORES | en | de | 21.11 | 51.77 |
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+ | FLORES | en | es | 18.61 | 48.02 |
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+ | FLORES | en | it | 16.63 | 47.24 |
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+ | ACL6060 | en | fr | 34.86 | 60.48 |
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+ | FLORES | en | fr | 24 | 55.36 |
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+
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+ ### SSUM Metrics
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+
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+ | testset | language | R-1_F1 | R-2_F1 | R-L_F1 |
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+ |:----------|:-----------|---------:|---------:|---------:|
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+ | ICSI | en | 22.9 | 2.7 | 20.4 |
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+
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+ ### ST Metrics
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+
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+ | testset | source-language | target-language | bleu | chrf |
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+ |:----------|:------------------|:------------------|-------:|-------:|
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+ | ACL6060 | en | fr | 28.65 | 56.2 |
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+ | ACL6060 | en | de | 19.12 | 49.06 |
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+ | MUSTC | en | de | 16.98 | 45.48 |
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+ | MUSTC | en | it | 14.68 | 43.03 |
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+ | MUSTC | en | fr | 19.09 | 48.09 |
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+ | MUSTC | en | es | 20.42 | 49.07 |
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+
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+
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+ ## How to use
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+
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+ Currently, this model can only be used via our [`speechlmm`](https://github.com/meetween/speechlmm) codebase. Refer to the instructions there for more details.
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+
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+ Important: before you can use this model, you must follow these steps:
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+ 1. Download the SeamlessM4T v2 speech encoder weights:
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+ ```python
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+ from transformers import AutoProcessor, SeamlessM4Tv2Model
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+
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+ processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large")
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+ model = AutoModel.from_pretrained("facebook/seamless-m4t-v2-large")
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+
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+ processor.save_pretrained("path/to/some_directory_1")
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+ model.save_pretrained("path/to/some_directory_1")
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+ ```
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+ 2. Go to `config.json` and change the `audio_encoder._name_or_path` to `path/to/some_directory_1`
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+ 3. Download the Auto-AVSR video encoder weights from [here](https://drive.google.com/file/d/1shcWXUK2iauRhW9NbwCc25FjU1CoMm8i/view?usp=sharing) and put them in `path/to/some_directory_2`
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+ 4. Go to `config.json` and change the `video_encoder._name_or_path` to `path/to/some_directory_2/vsr_trlrs3vox2_base.pth`
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+
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+ ## Training and evaluation data
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+
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+ ### Monolingual
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+
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+ | TASK | Task name | Dataset | License | Metric(s) |
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+ | -------- | ---------------------------- | ------------------ | --------------- | ------------------------- |
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+ | **ASR** | Automatic Speech Recognition | **LibriHeavy** | CC-BY-4.0 | WER |
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+ | | | **CommonVoice** | Apache-2.0 | |
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+ | | | **LibriTTS** | ? | |
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+ | | | **Spoken SQUAD** | CC-BY-SA-4.0 | |
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+ | | | **Speech-Massive** | CC-BY-NC-SA-4.0 | |
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+ | **VSR** | Visual Speech Recognition | **LRS2-BBC** | Custom | WER / ? |
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+ | **TSUM** | Text Summarization | **AMI** | CC-BY-4.0 | Rouge-1, Rouge-2, Rouge-L |
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+ | | | **ICSI** | | |
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+ | **SSUM** | Speech Summarization | **AMI** | CC-BY-4.0 | Rouge-1, Rouge-2, Rouge-L |
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+ | | | **ICSI** | CC-BY-4.0 | |
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+ | **TTS** | Text-to-Speech Synthesis | **LibriTTS** | CC-BY-4.0 | WER, CER, SSIM, UTMOS |
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+ | | | **LJSpeech** | Public domain | |
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+ | **SQA** | Spoken Question Answering | **Spoken SQUAD** | CC-BY-SA-4.0 | Accuracy, Exact Match, F1 |
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+
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+ ### Multilingual
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+
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+ | TASK | Task name | Dataset | License | Metric(s) |
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+ | ---------------- | ----------------------------- | ------------------------------------ | ------------------------------------------ | ------------------- |
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+ | **ST** | Speech-to-text Translation | **CoVoST2** | CC0 | BLEU, COMET, BLEURT |
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+ | | | **FLEURS** | CC-BY-4.0 | |
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+ | | | **EuroParl-ST** | CC-BY-NC-4.0 | |
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+ | | | **ACL 60/60** | CC-BY-4.0 | |
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+ | **MT** | Machine Translation | **FLORES** | CC-BY-SA-4.0 | BLEU, COMET, BLEURT |
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+ | | | **ACL 60/60** | CC-BY-4.0 | |
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+ | | | **EuroParl-ST** | CC-BY-NC-4.0 | |
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+ | **TextInstruct** | Text Instruction Following | **Everything_Instruct_Multilingual** | Apache-2.0 | MMLU |
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+ | **SLU** | Spoken Language Understanding | **Speech-Massive** | CC-BY-NC-SA-4.0 | Intent Accuracy |
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+ | | | **SLURP** | CC BY 4.0 (text) <br> CC BY-NC 4.0 (audio) | |
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+
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+ ## Framework versions
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+
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+ - Transformers 4.45.0
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+ - Pytorch 2.3.1+cu124.post2
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+ - Datasets 3.2.0
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+ - Tokenizers 0.20.0
config.json ADDED
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+ {
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+ "_name_or_path": "/net/scratch/hscra/plgrid/plgstefanop/checkpoints/speechlmm-v1/llava-pretrain-audio-seamless-qformer-auto_avsr-mlp-llama_3_1b-speechlmm/v1/s/checkpoint-600",
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+ "add_all_multimodal_tokens": true,
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+ "add_lm_head": true,
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+ "align_text_to_audio": false,
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+ "architectures": [
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+ "LlavaModel"
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+ ],
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+ "audio_adapter": {
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+ "add_cross_attention": true,
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+ "attention_probs_dropout_prob": 0.1,
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+ "compress_factor": 2,
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+ "cross_attention_every_n_layers": 1,
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "qformer",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 4,
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+ "num_queries": 1,
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+ "transformers_version": "4.45.0",
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+ "triplet_loss": false,
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+ },
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+ "adaptor_kernel_size": 8,
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+ "adaptor_stride": 8,
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+ "add_adapter": true,
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+ "architectures": [
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+ "SeamlessM4Tv2SpeechEncoder"
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+ ],
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+ "attention_dropout": 0.1,
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+ "bos_token_id": 2,
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+ "char_vocab_size": 10943,
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+ "conv_depthwise_kernel_size": 31,
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+ "eos_token_id": 3,
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+ "feature_projection_input_dim": 160,
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+ "hidden_size": 1024,
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+ "initializer_range": 0.02,
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+ "is_encoder_decoder": true,
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+ "lang_embed_dim": 256,
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+ "layer_norm_eps": 1e-05,
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+ "leaky_relu_slope": 0.1,
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+ "left_max_position_embeddings": 64,
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+ "max_new_tokens": 256,
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+ "max_position_embeddings": 4096,
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+ "model_type": "seamless_m4t_v2",
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+ "num_adapter_layers": 1,
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+ "num_attention_heads": 16,
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+ "num_hidden_layers": 24,
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+ "pad_token_id": 0,
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+ "position_embeddings_type": "relative_key",
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+ "resblock_dilation_sizes": [
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+ "speech_encoder_dropout": 0.0,
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+ "speech_encoder_hidden_act": "swish",
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+ "speech_encoder_intermediate_size": 4096,
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+ "speech_encoder_layerdrop": 0.1,
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+ "speech_encoder_layers": 24,
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+ "speech_encoder_left_chunk_num": 128,
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+ "spkr_embed_dim": 256,
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+ "t2u_bos_token_id": 0,
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+ "t2u_decoder_attention_heads": 16,
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+ "t2u_decoder_ffn_dim": 8192,
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+ "t2u_decoder_layers": 6,
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+ "t2u_encoder_attention_heads": 16,
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+ "t2u_encoder_ffn_dim": 8192,
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+ "t2u_encoder_layers": 6,
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+ "t2u_eos_token_id": 2,
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+ "t2u_max_position_embeddings": 4096,
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+ "t2u_pad_token_id": 1,
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+ "t2u_variance_pred_dropout": 0.5,
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+ "t2u_variance_predictor_embed_dim": 1024,
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+ "t2u_variance_predictor_hidden_dim": 256,
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+ "bos_token": "<|begin_of_text|>",
2091
+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2092
+ "clean_up_tokenization_spaces": true,
2093
+ "eos_token": "<|eot_id|>",
2094
+ "model_input_names": [
2095
+ "input_ids",
2096
+ "attention_mask"
2097
+ ],
2098
+ "model_max_length": 131072,
2099
+ "pad_token": "<|finetune_right_pad_id|>",
2100
+ "padding_side": "right",
2101
+ "tokenizer_class": "PreTrainedTokenizerFast"
2102
+ }