DiVA 1B

LICENSE

@@ -0,0 +1,373 @@
+Mozilla Public License Version 2.0
+==================================
+
+1. Definitions
+--------------
+
+1.1. "Contributor"
+    means each individual or legal entity that creates, contributes to
+    the creation of, or owns Covered Software.
+
+1.2. "Contributor Version"
+    means the combination of the Contributions of others (if any) used
+    by a Contributor and that particular Contributor's Contribution.
+
+1.3. "Contribution"
+    means Covered Software of a particular Contributor.
+
+1.4. "Covered Software"
+    means Source Code Form to which the initial Contributor has attached
+    the notice in Exhibit A, the Executable Form of such Source Code
+    Form, and Modifications of such Source Code Form, in each case
+    including portions thereof.
+
+1.5. "Incompatible With Secondary Licenses"
+    means
+
+    (a) that the initial Contributor has attached the notice described
+        in Exhibit B to the Covered Software; or
+
+    (b) that the Covered Software was made available under the terms of
+        version 1.1 or earlier of the License, but not also under the
+        terms of a Secondary License.
+
+1.6. "Executable Form"
+    means any form of the work other than Source Code Form.
+
+1.7. "Larger Work"
+    means a work that combines Covered Software with other material, in
+    a separate file or files, that is not Covered Software.
+
+1.8. "License"
+    means this document.
+
+1.9. "Licensable"
+    means having the right to grant, to the maximum extent possible,
+    whether at the time of the initial grant or subsequently, any and
+    all of the rights conveyed by this License.
+
+1.10. "Modifications"
+    means any of the following:
+
+    (a) any file in Source Code Form that results from an addition to,
+        deletion from, or modification of the contents of Covered
+        Software; or
+
+    (b) any new file in Source Code Form that contains any Covered
+        Software.
+
+1.11. "Patent Claims" of a Contributor
+    means any patent claim(s), including without limitation, method,
+    process, and apparatus claims, in any patent Licensable by such
+    Contributor that would be infringed, but for the grant of the
+    License, by the making, using, selling, offering for sale, having
+    made, import, or transfer of either its Contributions or its
+    Contributor Version.
+
+1.12. "Secondary License"
+    means either the GNU General Public License, Version 2.0, the GNU
+    Lesser General Public License, Version 2.1, the GNU Affero General
+    Public License, Version 3.0, or any later versions of those
+    licenses.
+
+1.13. "Source Code Form"
+    means the form of the work preferred for making modifications.
+
+1.14. "You" (or "Your")
+    means an individual or a legal entity exercising rights under this
+    License. For legal entities, "You" includes any entity that
+    controls, is controlled by, or is under common control with You. For
+    purposes of this definition, "control" means (a) the power, direct
+    or indirect, to cause the direction or management of such entity,
+    whether by contract or otherwise, or (b) ownership of more than
+    fifty percent (50%) of the outstanding shares or beneficial
+    ownership of such entity.
+
+2. License Grants and Conditions
+--------------------------------
+
+2.1. Grants
+
+Each Contributor hereby grants You a world-wide, royalty-free,
+non-exclusive license:
+
+(a) under intellectual property rights (other than patent or trademark)
+    Licensable by such Contributor to use, reproduce, make available,
+    modify, display, perform, distribute, and otherwise exploit its
+    Contributions, either on an unmodified basis, with Modifications, or
+    as part of a Larger Work; and
+
+(b) under Patent Claims of such Contributor to make, use, sell, offer
+    for sale, have made, import, and otherwise transfer either its
+    Contributions or its Contributor Version.
+
+2.2. Effective Date
+
+The licenses granted in Section 2.1 with respect to any Contribution
+become effective for each Contribution on the date the Contributor first
+distributes such Contribution.
+
+2.3. Limitations on Grant Scope
+
+The licenses granted in this Section 2 are the only rights granted under
+this License. No additional rights or licenses will be implied from the
+distribution or licensing of Covered Software under this License.
+Notwithstanding Section 2.1(b) above, no patent license is granted by a
+Contributor:
+
+(a) for any code that a Contributor has removed from Covered Software;
+    or
+
+(b) for infringements caused by: (i) Your and any other third party's
+    modifications of Covered Software, or (ii) the combination of its
+    Contributions with other software (except as part of its Contributor
+    Version); or
+
+(c) under Patent Claims infringed by Covered Software in the absence of
+    its Contributions.
+
+This License does not grant any rights in the trademarks, service marks,
+or logos of any Contributor (except as may be necessary to comply with
+the notice requirements in Section 3.4).
+
+2.4. Subsequent Licenses
+
+No Contributor makes additional grants as a result of Your choice to
+distribute the Covered Software under a subsequent version of this
+License (see Section 10.2) or under the terms of a Secondary License (if
+permitted under the terms of Section 3.3).
+
+2.5. Representation
+
+Each Contributor represents that the Contributor believes its
+Contributions are its original creation(s) or it has sufficient rights
+to grant the rights to its Contributions conveyed by this License.
+
+2.6. Fair Use
+
+This License is not intended to limit any rights You have under
+applicable copyright doctrines of fair use, fair dealing, or other
+equivalents.
+
+2.7. Conditions
+
+Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted
+in Section 2.1.
+
+3. Responsibilities
+-------------------
+
+3.1. Distribution of Source Form
+
+All distribution of Covered Software in Source Code Form, including any
+Modifications that You create or to which You contribute, must be under
+the terms of this License. You must inform recipients that the Source
+Code Form of the Covered Software is governed by the terms of this
+License, and how they can obtain a copy of this License. You may not
+attempt to alter or restrict the recipients' rights in the Source Code
+Form.
+
+3.2. Distribution of Executable Form
+
+If You distribute Covered Software in Executable Form then:
+
+(a) such Covered Software must also be made available in Source Code
+    Form, as described in Section 3.1, and You must inform recipients of
+    the Executable Form how they can obtain a copy of such Source Code
+    Form by reasonable means in a timely manner, at a charge no more
+    than the cost of distribution to the recipient; and
+
+(b) You may distribute such Executable Form under the terms of this
+    License, or sublicense it under different terms, provided that the
+    license for the Executable Form does not attempt to limit or alter
+    the recipients' rights in the Source Code Form under this License.
+
+3.3. Distribution of a Larger Work
+
+You may create and distribute a Larger Work under terms of Your choice,
+provided that You also comply with the requirements of this License for
+the Covered Software. If the Larger Work is a combination of Covered
+Software with a work governed by one or more Secondary Licenses, and the
+Covered Software is not Incompatible With Secondary Licenses, this
+License permits You to additionally distribute such Covered Software
+under the terms of such Secondary License(s), so that the recipient of
+the Larger Work may, at their option, further distribute the Covered
+Software under the terms of either this License or such Secondary
+License(s).
+
+3.4. Notices
+
+You may not remove or alter the substance of any license notices
+(including copyright notices, patent notices, disclaimers of warranty,
+or limitations of liability) contained within the Source Code Form of
+the Covered Software, except that You may alter any license notices to
+the extent required to remedy known factual inaccuracies.
+
+3.5. Application of Additional Terms
+
+You may choose to offer, and to charge a fee for, warranty, support,
+indemnity or liability obligations to one or more recipients of Covered
+Software. However, You may do so only on Your own behalf, and not on
+behalf of any Contributor. You must make it absolutely clear that any
+such warranty, support, indemnity, or liability obligation is offered by
+You alone, and You hereby agree to indemnify every Contributor for any
+liability incurred by such Contributor as a result of warranty, support,
+indemnity or liability terms You offer. You may include additional
+disclaimers of warranty and limitations of liability specific to any
+jurisdiction.
+
+4. Inability to Comply Due to Statute or Regulation
+---------------------------------------------------
+
+If it is impossible for You to comply with any of the terms of this
+License with respect to some or all of the Covered Software due to
+statute, judicial order, or regulation then You must: (a) comply with
+the terms of this License to the maximum extent possible; and (b)
+describe the limitations and the code they affect. Such description must
+be placed in a text file included with all distributions of the Covered
+Software under this License. Except to the extent prohibited by statute
+or regulation, such description must be sufficiently detailed for a
+recipient of ordinary skill to be able to understand it.
+
+5. Termination
+--------------
+
+5.1. The rights granted under this License will terminate automatically
+if You fail to comply with any of its terms. However, if You become
+compliant, then the rights granted under this License from a particular
+Contributor are reinstated (a) provisionally, unless and until such
+Contributor explicitly and finally terminates Your grants, and (b) on an
+ongoing basis, if such Contributor fails to notify You of the
+non-compliance by some reasonable means prior to 60 days after You have
+come back into compliance. Moreover, Your grants from a particular
+Contributor are reinstated on an ongoing basis if such Contributor
+notifies You of the non-compliance by some reasonable means, this is the
+first time You have received notice of non-compliance with this License
+from such Contributor, and You become compliant prior to 30 days after
+Your receipt of the notice.
+
+5.2. If You initiate litigation against any entity by asserting a patent
+infringement claim (excluding declaratory judgment actions,
+counter-claims, and cross-claims) alleging that a Contributor Version
+directly or indirectly infringes any patent, then the rights granted to
+You by any and all Contributors for the Covered Software under Section
+2.1 of this License shall terminate.
+
+5.3. In the event of termination under Sections 5.1 or 5.2 above, all
+end user license agreements (excluding distributors and resellers) which
+have been validly granted by You or Your distributors under this License
+prior to termination shall survive termination.
+
+************************************************************************
+*                                                                      *
+*  6. Disclaimer of Warranty                                           *
+*  -------------------------                                           *
+*                                                                      *
+*  Covered Software is provided under this License on an "as is"       *
+*  basis, without warranty of any kind, either expressed, implied, or  *
+*  statutory, including, without limitation, warranties that the       *
+*  Covered Software is free of defects, merchantable, fit for a        *
+*  particular purpose or non-infringing. The entire risk as to the     *
+*  quality and performance of the Covered Software is with You.        *
+*  Should any Covered Software prove defective in any respect, You     *
+*  (not any Contributor) assume the cost of any necessary servicing,   *
+*  repair, or correction. This disclaimer of warranty constitutes an   *
+*  essential part of this License. No use of any Covered Software is   *
+*  authorized under this License except under this disclaimer.         *
+*                                                                      *
+************************************************************************
+
+************************************************************************
+*                                                                      *
+*  7. Limitation of Liability                                          *
+*  --------------------------                                          *
+*                                                                      *
+*  Under no circumstances and under no legal theory, whether tort      *
+*  (including negligence), contract, or otherwise, shall any           *
+*  Contributor, or anyone who distributes Covered Software as          *
+*  permitted above, be liable to You for any direct, indirect,         *
+*  special, incidental, or consequential damages of any character      *
+*  including, without limitation, damages for lost profits, loss of    *
+*  goodwill, work stoppage, computer failure or malfunction, or any    *
+*  and all other commercial damages or losses, even if such party      *
+*  shall have been informed of the possibility of such damages. This   *
+*  limitation of liability shall not apply to liability for death or   *
+*  personal injury resulting from such party's negligence to the       *
+*  extent applicable law prohibits such limitation. Some               *
+*  jurisdictions do not allow the exclusion or limitation of           *
+*  incidental or consequential damages, so this exclusion and          *
+*  limitation may not apply to You.                                    *
+*                                                                      *
+************************************************************************
+
+8. Litigation
+-------------
+
+Any litigation relating to this License may be brought only in the
+courts of a jurisdiction where the defendant maintains its principal
+place of business and such litigation shall be governed by laws of that
+jurisdiction, without reference to its conflict-of-law provisions.
+Nothing in this Section shall prevent a party's ability to bring
+cross-claims or counter-claims.
+
+9. Miscellaneous
+----------------
+
+This License represents the complete agreement concerning the subject
+matter hereof. If any provision of this License is held to be
+unenforceable, such provision shall be reformed only to the extent
+necessary to make it enforceable. Any law or regulation which provides
+that the language of a contract shall be construed against the drafter
+shall not be used to construe this License against a Contributor.
+
+10. Versions of the License
+---------------------------
+
+10.1. New Versions
+
+William Held, Diyi Yang, Georgia Tech, and Stanford University are the license stewards. Except as provided in Section
+10.3, no one other than the license steward has the right to modify or
+publish new versions of this License. Each version will be given a
+distinguishing version number.
+
+10.2. Effect of New Versions
+
+You may distribute the Covered Software under the terms of the version
+of the License under which You originally received the Covered Software,
+or under the terms of any subsequent version published by the license
+steward.
+
+10.3. Modified Versions
+
+If you create software not governed by this License, and you want to
+create a new license for such software, you may create and use a
+modified version of this License if you rename the license and remove
+any references to the name of the license steward (except to note that
+such modified license differs from this License).
+
+10.4. Distributing Source Code Form that is Incompatible With Secondary
+Licenses
+
+If You choose to distribute Source Code Form that is Incompatible With
+Secondary Licenses under the terms of this version of the License, the
+notice described in Exhibit B of this License must be attached.
+
+Exhibit A - Source Code Form License Notice
+-------------------------------------------
+
+  This Source Code Form is subject to the terms of the Mozilla Public
+  License, v. 2.0. If a copy of the MPL was not distributed with this
+  file, You can obtain one at https://mozilla.org/MPL/2.0/.
+
+If it is not possible or desirable to put the notice in a particular
+file, then You may include the notice in a location (such as a LICENSE
+file in a relevant directory) where a recipient would be likely to look
+for such a notice.
+
+You may add additional accurate notices of copyright ownership.
+
+Exhibit B - "Incompatible With Secondary Licenses" Notice
+---------------------------------------------------------
+
+  This Source Code Form is "Incompatible With Secondary Licenses", as
+  defined by the Mozilla Public License, v. 2.0.

README.md

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+---
+license: mpl-2.0
+datasets:
+- mozilla-foundation/common_voice_17_0
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+---
+# Model Card for Diva Llama 3
+
+<!-- Provide a quick summary of what the model is/does. [Optional] -->
+This is an end-to-end Voice Assistant Model which can handle speech and text as inputs. It is trained using distillation loss. More details in the [pre-print](https://arxiv.org/abs/2410.02678) here.
+
+See the model in action at [diva-audio.github.io](https://diva-audio.github.io) or look at the full training logs on [Weights&Biases](https://wandb.ai/i18nlp/DiVA%20Training%20Runs/runs/gqpwnd99?nw=nwuserheld).
+
+## Citation
+**BibTeX:**
+
+```
+@misc{DiVA,
+      title={{D}istilling an {E}nd-to-{E}nd {V}oice {A}ssistant {W}ithout {I}nstruction {T}raining {D}ata}, 
+      author={William Held and Ella Li and Michael Ryan and Weiyan Shi and Yanzhe Zhang and Diyi Yang},
+      year={2024},
+      eprint={2410.02678},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2410.02678}, 
+}
+    
+```
+
+### Inference Example
+```python
+from transformers import AutoModel
+import librosa
+import wget
+
+filename = wget.download(
+    "https://github.com/ffaisal93/SD-QA/raw/refs/heads/master/dev/eng/irl/wav_eng/-1008642825401516622.wav"
+)
+
+speech_data, _ = librosa.load(filename, sr=16_000)
+
+model = AutoModel.from_pretrained("WillHeld/DiVA-llama-3-v0-8b", trust_remote_code=True)
+
+print(model.generate([speech_data]))
+print(model.generate([speech_data], ["Reply Briefly Like A Pirate"]))
+
+filename = wget.download(
+    "https://github.com/ffaisal93/SD-QA/raw/refs/heads/master/dev/eng/irl/wav_eng/-2426554427049983479.wav"
+)
+
+speech_data2, _ = librosa.load(filename, sr=16_000)
+
+print(
+    model.generate(
+        [speech_data, speech_data2],
+        ["Reply Briefly Like A Pirate", "Reply Briefly Like A New Yorker"],
+    )
+)
+```
+
+##  Table of Contents
+
+- [Model Card for DiVA Llama 3](#model-card-for-DiVA-Llama-3)
+- [Citation](#citation)
+- [Table of Contents](#table-of-contents)
+- [Training Details](#training-details)
+  - [Training Data](#training-data)
+  - [Training Procedure](#training-procedure)
+- [Environmental Impact](#environmental-impact)
+- [Technical Specifications [optional]](#technical-specifications-optional)
+  - [Model Architecture and Objective](#model-architecture-and-objective)
+  - [Compute Infrastructure](#compute-infrastructure)
+    - [Hardware](#hardware)
+    - [Software](#software)
+- [Model Card Contact](#model-card-contact)
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+This model was trained on the [CommonVoice](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1) corpus.
+
+
+### Training Procedure
+
+This model was trained for 7k gradient steps with a batch size of 512 Recordings and a linearly decaying learning rate from 5e-5 to zero, with a linear warmup of 70 steps.
+
+### Environmental Impact
+
+- **Hardware Type:** V4-256 TPU
+- **Hours used:** 11 Hours
+- **Cloud Provider:** Google Cloud.
+- **Compute Region:** US Central C
+
+
+### Hardware
+
+This model was trained on at V4-256 TPU on Google Cloud.
+
+### Software
+
+This model was trained with [Levanter](https://github.com/stanford-crfm/levanter) 
+
+
+## Model Card Authors [optional]
+
+<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
+
+Will Held
+
+## Model Card Contact
+
+[email protected]

config.json

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+{"return_dict": true, "output_hidden_states": false, "output_attentions": false, "torchscript": false, "torch_dtype": null, "use_bfloat16": false, "tf_legacy_loss": false, "pruned_heads": {}, "tie_word_embeddings": true, "chunk_size_feed_forward": 0, "is_encoder_decoder": false, "is_decoder": false, "cross_attention_hidden_size": null, "add_cross_attention": false, "tie_encoder_decoder": false, "max_length": 20, "min_length": 0, "do_sample": false, "early_stopping": false, "num_beams": 1, "num_beam_groups": 1, "diversity_penalty": 0.0, "temperature": 1.0, "top_k": 50, "top_p": 1.0, "typical_p": 1.0, "repetition_penalty": 1.0, "length_penalty": 1.0, "no_repeat_ngram_size": 0, "encoder_no_repeat_ngram_size": 0, "bad_words_ids": null, "num_return_sequences": 1, "output_scores": false, "return_dict_in_generate": false, "forced_bos_token_id": null, "forced_eos_token_id": null, "remove_invalid_values": false, "exponential_decay_length_penalty": null, "suppress_tokens": null, "begin_suppress_tokens": [128000, 128009], "architectures": ["DiVAModel"], "finetuning_task": null, "id2label": {"0": "LABEL_0", "1": "LABEL_1"}, "label2id": {"LABEL_0": 0, "LABEL_1": 1}, "tokenizer_class": null, "prefix": null, "bos_token_id": 128000, "pad_token_id": null, "eos_token_id": 128009, "sep_token_id": null, "decoder_start_token_id": 128000, "task_specific_params": null, "problem_type": null, "_name_or_path": "", "_attn_implementation_autoset": false, "transformers_version": "4.46.0", "model_type": "", "auto_map": {"AutoConfig": "WillHeld/DiVA-llama-3-v0-8b--configuring_diva.DiVAConfig", "AutoModel": "WillHeld/DiVA-llama-3-v0-8b--modeling_diva.DiVAModel"}, "vocab_size": 128256, "reference_encoder": "openai/whisper-large-v3", "reference_decoder": "meta-llama/Llama-3.2-1B-Instruct"}

configuring_diva.py

@@ -0,0 +1,11 @@
+from transformers import PretrainedConfig
+
+
+class DiVAConfig(PretrainedConfig):
+    model_type = "diva"
+
+    def __init__(
+        self,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cfd18210cdd7a8ab807ea213ac42227caf198fba318b68bbb4e5c1cecb5ea48c
+size 9937324280

modeling_diva.py

@@ -0,0 +1,380 @@
+import copy
+import json
+import os
+from typing import Optional, Union
+
+import librosa
+import numpy as np
+import torch
+import torch.nn.functional as F
+from datasets import Audio
+from safetensors.torch import load, load_model
+from torch import nn
+from .configuring_diva import DiVAConfig
+from transformers import (
+    AutoProcessor,
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    PreTrainedModel,
+    WhisperModel,
+)
+
+
+class WhisperConnector(nn.Module):
+    def __init__(
+        self,
+    ):
+        super().__init__()
+        self.decoder = None
+        self.projection = nn.Linear(1280, 4096)
+        self.query_tokens = nn.Parameter(torch.randn(448, 1280))
+
+    def forward(self, x, output_device="cuda:1"):
+        bsz = x.shape[0]
+        query_tokens = self.query_tokens[None, :, :].expand(bsz, -1, -1)
+        virt_whisper_tokens = self.decoder(
+            inputs_embeds=query_tokens, encoder_hidden_states=x
+        )
+        if self.projection.weight.shape[-1] == 5120:
+            virtual_tokens = self.projection(virt_whisper_tokens[0].reshape(112, 5120))
+        else:
+            virtual_tokens = self.projection(virt_whisper_tokens[0])
+        return virtual_tokens.to(output_device)
+
+
+class DiVAModel(PreTrainedModel):
+    config_class = DiVAConfig
+
+    def __init__(
+        self, via_path=None, config_dict={}, device_map=None, speech_encoder_device=None
+    ):
+        super().__init__(DiVAConfig.from_dict(config_dict))
+        if speech_encoder_device is None:
+            speech_encoder_device = "cuda:0"
+        whisper = WhisperModel.from_pretrained(config_dict["reference_encoder"])
+        connector = WhisperConnector()
+        connector.decoder = copy.deepcopy(whisper.decoder)
+        if via_path is not None:
+            with open(via_path, "rb") as f:
+                sd = load(f.read())
+
+            with torch.no_grad():
+                connector.query_tokens = nn.Parameter(sd["query_tokens"])
+                connector.projection.weight = nn.Parameter(sd["projection.weight"].T)
+                connector.projection.bias = nn.Parameter(sd["projection.bias"])
+                wsd = {
+                    key.replace("connector.", ""): sd[key]
+                    for key in sd
+                    if key.startswith("connector.")
+                }
+                connector.decoder.load_state_dict(wsd)
+
+        if device_map == None:
+            num_layers = 32
+            num_gpus = 2
+            device_map = dict(
+                **{"model.embed_tokens": 1, "model.norm": 1, "lm_head": 2},
+                **{
+                    "model.layers." + str(i): 1 + (i // (num_layers // num_gpus))
+                    for i in range(num_layers)
+                },
+            )
+
+        self.connector = connector.to(speech_encoder_device)
+        self.whisper_encoder = whisper.encoder.to(speech_encoder_device)
+        self.llm_decoder = AutoModelForCausalLM.from_pretrained(
+            config_dict["reference_decoder"],
+            device_map=device_map,
+            torch_dtype=torch.float16,
+        )
+        self.processor = AutoProcessor.from_pretrained(config_dict["reference_encoder"])
+        self.tokenizer = AutoTokenizer.from_pretrained(config_dict["reference_decoder"])
+        if self.tokenizer.pad_token_id == None:
+            override_token = list(self.tokenizer.added_tokens_decoder.items())[-1]
+            self.tokenizer.pad_token_id = override_token[0]
+            self.tokenizer.pad_tokn = str(override_token[1])
+        prefix, suffix = self.tokenizer.apply_chat_template(
+            [{"role": "user", "content": "PLACEHOLDER"}],
+            tokenize=False,
+            add_generation_prompt=True,
+        ).split("PLACEHOLDER")
+        non_null = [line for line in prefix.split("\n") if line.strip()]
+        prefix_tok = self.tokenizer.encode(prefix, add_special_tokens=False)
+        suffix_tok = self.tokenizer.encode(suffix, add_special_tokens=False)
+        self.prefix = torch.tensor(prefix_tok).to(
+            self.llm_decoder.model.embed_tokens.weight.device
+        )
+
+        self.pre_system = torch.tensor(
+            self.tokenizer.encode(non_null[0] + "\n", add_special_tokens=False)
+        ).to(self.llm_decoder.model.embed_tokens.weight.device)
+        self.post_system = torch.tensor(
+            self.tokenizer.encode("\n" + non_null[-1] + "\n", add_special_tokens=False)
+        ).to(self.llm_decoder.model.embed_tokens.weight.device)
+        self.final_header = torch.tensor(suffix_tok).to(
+            self.llm_decoder.model.embed_tokens.weight.device
+        )
+        self.speech_encoder_device = speech_encoder_device
+
+    def can_generate(cls):
+        return False
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+        *model_args,
+        config=None,
+        cache_dir=None,
+        **kwargs,
+    ):
+        if os.path.isdir(pretrained_model_name_or_path):
+            via_path = pretrained_model_name_or_path + "/model.safetensors"
+            config_path = pretrained_model_name_or_path + "/config.json"
+        else:
+            # Loading from huggingface repo
+            from huggingface_hub import hf_hub_download
+
+            hf_hub_download(
+                repo_id=pretrained_model_name_or_path,
+                filename="model.safetensors",
+                token=kwargs.get("token", None),
+                local_dir=os.path.dirname(__file__),
+            )
+            hf_hub_download(
+                repo_id=pretrained_model_name_or_path,
+                filename="config.json",
+                token=kwargs.get("token", None),
+                local_dir=os.path.dirname(__file__),
+            )
+            via_path = os.path.dirname(__file__) + "/model.safetensors"
+            config_path = os.path.dirname(__file__) + "/config.json"
+        with open(config_path, "r") as f:
+            config_dict = json.loads(f.read())
+        return cls(
+            via_path,
+            config_dict,
+            kwargs["device_map"] if "device_map" in kwargs else "auto",
+            (
+                kwargs["speech_encoder_device"]
+                if "speech_encoder_device" in kwargs
+                else None
+            ),
+        )
+
+    def forward(self, audio, prefix_text_tokens, suffix_text_tokens):
+        inputs = self.processor(audio, return_tensors="pt", sampling_rate=16_000)
+        input_features = inputs.input_features.to(self.speech_encoder_device)
+        hidden_states = self.whisper_encoder(input_features=input_features)[
+            "last_hidden_state"
+        ]
+        virt_tokens = self.connector(
+            hidden_states,
+            output_device=self.llm_decoder.model.embed_tokens.weight.device,
+        ).squeeze()
+
+        prefix_embed = self.llm_decoder.model.embed_tokens(prefix_text_tokens)
+        suffix_embed = self.llm_decoder.model.embed_tokens(suffix_text_tokens)
+        inputs_embeds = torch.cat(
+            [prefix_embed, virt_tokens, suffix_embed], axis=0
+        ).unsqueeze(0)
+
+        outputs = self.llm_decoder(
+            inputs_embeds=inputs_embeds.to(
+                self.llm_decoder.model.embed_tokens.weight.device
+            ).half(),
+            return_dict=True,
+            output_hidden_states=True,
+            past_key_values=past_key_values,
+        )
+
+        return outputs
+
+    @torch.no_grad()
+    def generate(
+        self,
+        audio,
+        text_prompt=None,
+        do_sample=False,
+        logits_processor=None,
+        max_new_tokens=128,
+    ):
+        inputs = self.processor(audio, return_tensors="pt", sampling_rate=16_000)
+        input_features = inputs.input_features.to(self.speech_encoder_device)
+        hidden_states = self.whisper_encoder(input_features=input_features)[
+            "last_hidden_state"
+        ]
+        virt_tokens = self.connector(
+            hidden_states,
+            output_device=self.llm_decoder.model.embed_tokens.weight.device,
+        )
+        bsz = virt_tokens.shape[0]
+
+        if text_prompt != None and text_prompt != "":
+            user_prompt_text = torch.tensor(
+                self.tokenizer(
+                    text_prompt,
+                    add_special_tokens=False,
+                    padding=True,
+                    padding_side="right",
+                )["input_ids"],
+                device=self.pre_system.device,
+            )
+            prefix = torch.cat(
+                [
+                    self.pre_system.expand(
+                        bsz,
+                        -1,
+                    ),
+                    user_prompt_text,
+                    self.post_system.expand(
+                        bsz,
+                        -1,
+                    ),
+                ],
+                axis=1,
+            )
+        else:
+            prefix = self.prefix
+        prefix_embed = self.llm_decoder.model.embed_tokens(prefix).expand(bsz, -1, -1)
+        suffix = self.final_header
+        suffix_embed = self.llm_decoder.model.embed_tokens(suffix).expand(bsz, -1, -1)
+        inputs_embeds = torch.cat([prefix_embed, virt_tokens, suffix_embed], axis=1)
+        outs = [[] for i in range(bsz)]
+        complete = [False] * bsz
+        outputs = None
+        greedy = 1
+        i = 0
+        while not all(complete) and len(outs[0]) < max_new_tokens:
+            past_key_values = outputs.past_key_values if outputs else None
+            outputs = self.llm_decoder(
+                inputs_embeds=inputs_embeds.to(
+                    self.llm_decoder.model.embed_tokens.weight.device
+                ).half(),
+                return_dict=True,
+                output_hidden_states=True,
+                past_key_values=past_key_values,
+            )
+            next_token_logits = outputs.logits[:, -1, :]
+
+            if logits_processor:
+                local_outs = torch.tensor(outs) if outs != [] else suffix
+                local_outs = local_outs.reshape(1, -1)
+                next_token_logits = logits_processor(
+                    local_outs,
+                    next_token_logits.reshape(1, -1),
+                )
+                next_token_logits = next_token_logits.flatten()
+            if do_sample:
+                logits = next_token_logits / temperature
+                probs = F.softmax(logits, dim=-1)
+                greedy = torch.multinomial(probs, num_samples=1)[0]
+            else:
+                greedy = next_token_logits.argmax(dim=-1)
+            for token_index, out in enumerate(greedy.flatten().tolist()):
+                if not complete[token_index]:
+                    outs[token_index].append(out)
+                if out == 128009:
+                    complete[token_index] = True
+
+            next_embed = self.llm_decoder.model.embed_tokens(greedy.reshape(-1, 1))
+            inputs_embeds = next_embed
+        return self.tokenizer.batch_decode(outs, skip_special_tokens=True)
+
+    def generate_stream(
+        self,
+        audio,
+        text_prompt,
+        do_sample=False,
+        logits_processor=None,
+        max_new_tokens=128,
+        return_outputs=False,
+        init_outputs=None,
+    ):
+        inputs = self.processor(audio, return_tensors="pt", sampling_rate=16_000)
+        input_features = inputs.input_features.to(self.whisper_encoder.device)
+        hidden_states = self.whisper_encoder(input_features=input_features)[
+            "last_hidden_state"
+        ]
+        virt_tokens = self.connector(
+            hidden_states,
+            output_device=self.llm_decoder.model.embed_tokens.weight.device,
+        ).squeeze()
+
+        if text_prompt != None and text_prompt != "":
+            user_prompt_text = torch.tensor(
+                self.tokenizer(
+                    text_prompt,
+                    add_special_tokens=False,
+                    padding=True,
+                    padding_side="right",
+                )["input_ids"],
+                device=self.pre_system.device,
+            )
+            prefix = torch.cat(
+                [self.pre_system, user_prompt_text, self.post_system],
+                axis=0,
+            )
+        else:
+            prefix = self.prefix
+        prefix_embed = self.llm_decoder.model.embed_tokens(prefix)
+        suffix = self.final_header
+        suffix_embed = self.llm_decoder.model.embed_tokens(suffix)
+        inputs_embeds = torch.cat(
+            [prefix_embed, virt_tokens, suffix_embed], axis=0
+        ).unsqueeze(0)
+        outs = []
+        outputs = init_outputs
+        greedy = 1
+        i = 0
+        while greedy != 128009 and len(outs) < max_new_tokens:
+            past_key_values = outputs.past_key_values if outputs else None
+            outputs = self.llm_decoder(
+                inputs_embeds=inputs_embeds.to(
+                    self.llm_decoder.model.embed_tokens.weight.device
+                ).half(),
+                return_dict=True,
+                output_hidden_states=True,
+                past_key_values=past_key_values,
+            )
+            next_token_logits = outputs.logits[-1, -1, :]
+
+            if logits_processor:
+                local_outs = torch.tensor(outs) if outs != [] else suffix
+                local_outs = local_outs.reshape(1, -1)
+                next_token_logits = logits_processor(
+                    local_outs,
+                    next_token_logits.reshape(1, -1),
+                )
+                next_token_logits = next_token_logits.flatten()
+            if do_sample:
+                logits = next_token_logits / temperature
+                probs = F.softmax(logits, dim=-1)
+                greedy = torch.multinomial(probs, num_samples=1)[0]
+            else:
+                greedy = next_token_logits.argmax()
+            outs.append(greedy)
+            next_embed = self.llm_decoder.model.embed_tokens(greedy.reshape(1, 1))
+            inputs_embeds = next_embed
+            if not return_outputs:
+                yield self.tokenizer.decode(outs, skip_special_tokens=True).replace(
+                    "<|eot_id|>", ""
+                )
+            else:
+                yield (
+                    self.tokenizer.decode(outs, skip_special_tokens=True).replace(
+                        "<|eot_id|>", ""
+                    ),
+                    outputs,
+                )
+        if not return_outputs:
+            return self.tokenizer.decode(outs, skip_special_tokens=True).replace(
+                "<|eot_id|>", ""
+            )
+        else:
+            return (
+                self.tokenizer.decode(outs, skip_special_tokens=True).replace(
+                    "<|eot_id|>", ""
+                ),
+                outputs,
+            )

preprocessor_config.json

@@ -0,0 +1,14 @@
+{
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}

special_tokens_map.json

@@ -0,0 +1,16 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

test.py

@@ -0,0 +1,28 @@
+from transformers import AutoModel
+import librosa
+import wget
+from modeling_diva import DiVAModel
+
+filename = wget.download(
+    "https://github.com/ffaisal93/SD-QA/raw/refs/heads/master/dev/eng/irl/wav_eng/-1008642825401516622.wav"
+)
+
+speech_data, _ = librosa.load(filename, sr=16_000)
+
+model = DiVAModel.from_pretrained("./")
+
+print(model.generate([speech_data]))
+print(model.generate([speech_data], ["Repeat verbatim what is said to you."]))
+
+filename = wget.download(
+    "https://github.com/ffaisal93/SD-QA/raw/refs/heads/master/dev/eng/irl/wav_eng/-2426554427049983479.wav"
+)
+
+speech_data2, _ = librosa.load(filename, sr=16_000)
+
+print(
+    model.generate(
+        [speech_data, speech_data2],
+        ["Reply Briefly Like A Pirate", "Reply Briefly Like A New Yorker"],
+    )
+)

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
+size 17209920

tokenizer_config.json

@@ -0,0 +1,2062 @@
+{
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128001": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128002": {
+      "content": "<|reserved_special_token_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128003": {
+      "content": "<|reserved_special_token_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128004": {
+      "content": "<|finetune_right_pad_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128005": {
+      "content": "<|reserved_special_token_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128006": {
+      "content": "<|start_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128007": {
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+      "special": true
+    },
+    "128008": {
+      "content": "<|eom_id|>",
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+    },
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+      "special": true
+    },
+    "128247": {
+      "content": "<|reserved_special_token_239|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128248": {
+      "content": "<|reserved_special_token_240|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128249": {
+      "content": "<|reserved_special_token_241|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128250": {
+      "content": "<|reserved_special_token_242|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128251": {
+      "content": "<|reserved_special_token_243|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_244|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128253": {
+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128254": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "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",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}