Add files using upload-large-folder tool

.gitattributes

@@ -1,35 +1,7 @@
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LICENSE

@@ -0,0 +1,201 @@
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MANIFEST.sha256

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README.md

@@ -0,0 +1,78 @@
+---
+language:
+- en
+- zh
+license: apache-2.0
+library_name: transformers
+pipeline_tag: audio-to-audio
+tags:
+- speech-to-speech
+- simultaneous-translation
+- streaming
+- qwen2.5-omni
+- peft
+---
+
+# SimulS2ST-Omni model assets
+
+Model assets for **SimulS2ST-Omni**, accepted by EMNLP 2026.
+
+This repository is organized by runtime role and contains no optimizer,
+scheduler, RNG, trainer-state, or step-numbered checkpoint directories.
+
+## Layout
+
+| Directory | Contents | Approximate size |
+|---|---|---:|
+| `offline/` | Fully merged OmniTalker model in standard sharded Transformers format, including processor and tokenizer files | 12 GB |
+| `simuls2st_adapter/` | Simultaneous S2ST thinker/talker LoRA adapters, talker components, and tokenizer files | 893 MB |
+| `voicebox/` | VoiceBox, Vocos, and `voicebox_config.json` | 2.3 GB |
+| `dualcodec/` | DualCodec weights and W2V-BERT normalization statistics | 321 MB |
+| `w2v/` | `facebook/w2v-bert-2.0` feature model used by the codec tokenizer | 2.2 GB |
+
+The root `simuls2st_config.json` records the canonical relative path for every
+runtime component. `MANIFEST.sha256` verifies all runtime assets.
+
+## Download
+
+```bash
+hf download HA-SA-ki/SimulS2ST-Omni \
+  --local-dir models/SimulS2ST-Omni
+```
+
+## Runtime paths
+
+```text
+models/SimulS2ST-Omni/offline
+models/SimulS2ST-Omni/simuls2st_adapter
+models/SimulS2ST-Omni/voicebox/voicebox.safetensors
+models/SimulS2ST-Omni/voicebox/vocos.safetensors
+models/SimulS2ST-Omni/voicebox/voicebox_config.json
+models/SimulS2ST-Omni/dualcodec/dualcodec.safetensors
+models/SimulS2ST-Omni/dualcodec/w2v_bert_stats.pt
+models/SimulS2ST-Omni/w2v
+```
+
+For offline inference, use `offline/` as both the model and checkpoint path.
+For simultaneous streaming inference, use `offline/` as the base model and
+`simuls2st_adapter/` as the adapter/checkpoint path.
+
+## License
+
+The SimulS2ST-Omni model weights are released under the
+[Apache License 2.0](LICENSE). Qwen, VoiceBox, DualCodec, W2V-BERT, and other
+upstream components retain their respective attribution notices and license
+conditions.
+
+The `w2v/` directory is a local copy of
+[`facebook/w2v-bert-2.0`](https://huggingface.co/facebook/w2v-bert-2.0), whose
+upstream model card declares the MIT license. A copy of that model card is kept
+at `w2v/UPSTREAM_README.md`.
+
+## Upload
+
+Upload large files with the Hugging Face CLI rather than a regular Git push:
+
+```bash
+hf upload-large-folder HA-SA-ki/SimulS2ST-Omni . --repo-type model
+```

dualcodec/dualcodec.safetensors

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+size 336105136

dualcodec/w2v_bert_stats.pt

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+oid sha256:c9c176c2b8850ab2e3ba828bbfa969deaf4566ce55db5f2687b8430b87526ad2
+size 9343

offline/added_tokens.json

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+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|AUDIO|>": 151646,
+  "<|IMAGE|>": 151655,
+  "<|VIDEO|>": 151656,
+  "<|audio_bos|>": 151647,
+  "<|audio_eos|>": 151648,
+  "<|box_end|>": 151649,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|vision_bos|>": 151652,
+  "<|vision_eos|>": 151653,
+  "<|vision_pad|>": 151654
+}

offline/chat_template.jinja

@@ -0,0 +1,7 @@
+{% set audio_count = namespace(value=0) %}{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
+You are a helpful assistant.<|im_end|>
+{% endif %}<|im_start|>{{ message['role'] }}
+{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
+{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_bos|><|IMAGE|><|vision_eos|>{% elif content['type'] == 'audio' or 'audio' in content or 'audio_url' in content %}{% set audio_count.value = audio_count.value + 1 %}{% if add_audio_id %}Audio {{ audio_count.value }}: {% endif %}<|audio_bos|><|AUDIO|><|audio_eos|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_bos|><|VIDEO|><|vision_eos|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
+{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
+{% endif %}

offline/config.json

@@ -0,0 +1,636 @@
+{
+  "architectures": [
+    "Qwen2_5OmniForConditionalGeneration"
+  ],
+  "dtype": "bfloat16",
+  "enable_audio_output": true,
+  "enable_talker": true,
+  "model_type": "qwen2_5_omni",
+  "talker_config": {
+    "_attn_implementation_autoset": true,
+    "_name_or_path": "Qwen2.5-Omni-3B/talker",
+    "architectures": [
+      "Qwen2OmniTalkerForConditionalGeneration"
+    ],
+    "attention_dropout": 0.0,
+    "audio_end_token_id": 151648,
+    "audio_start_token_id": 151647,
+    "audio_token_index": 151646,
+    "dtype": "bfloat16",
+    "embedding_size": 2048,
+    "head_dim": 64,
+    "hidden_act": "silu",
+    "hidden_size": 896,
+    "image_token_index": 151655,
+    "init_std": 0.02,
+    "initializer_range": 0.02,
+    "intermediate_size": 4864,
+    "layer_types": [
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
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+    "<|vision_eos|>",
+    "<|vision_pad|>",
+    "<|IMAGE|>",
+    "<|VIDEO|>"
+  ],
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+  "clean_up_tokenization_spaces": false,
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+  "errors": "replace",
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+    "audio_eos_token": "<|audio_eos|>",
+    "audio_token": "<|AUDIO|>",
+    "image_token": "<|IMAGE|>",
+    "video_token": "<|VIDEO|>",
+    "vision_bos_token": "<|vision_bos|>",
+    "vision_eos_token": "<|vision_eos|>"
+  },
+  "image_token": "<|IMAGE|>",
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
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+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
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+  "video_token": "<|VIDEO|>",
+  "vision_bos_token": "<|vision_bos|>",
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+}

simuls2st_adapter/added_tokens.json

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+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|AUDIO|>": 151646,
+  "<|IMAGE|>": 151655,
+  "<|VIDEO|>": 151656,
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+}

simuls2st_adapter/chat_template.jinja

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+{% set audio_count = namespace(value=0) %}{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
+You are a helpful assistant.<|im_end|>
+{% endif %}<|im_start|>{{ message['role'] }}
+{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
+{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_bos|><|IMAGE|><|vision_eos|>{% elif content['type'] == 'audio' or 'audio' in content or 'audio_url' in content %}{% set audio_count.value = audio_count.value + 1 %}{% if add_audio_id %}Audio {{ audio_count.value }}: {% endif %}<|audio_bos|><|AUDIO|><|audio_eos|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_bos|><|VIDEO|><|vision_eos|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
+{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
+{% endif %}

simuls2st_adapter/special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|AUDIO|>",
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+    "<|audio_eos|>",
+    "<|box_end|>",
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+    "<|quad_end|>",
+    "<|vision_bos|>",
+    "<|vision_eos|>",
+    "<|vision_pad|>",
+    "<|IMAGE|>",
+    "<|VIDEO|>"
+  ],
+  "audio_bos_token": "<|audio_bos|>",
+  "audio_eos_token": "<|audio_eos|>",
+  "audio_token": "<|AUDIO|>",
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
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+    "rstrip": false,
+    "single_word": false
+  },
+  "video_token": "<|VIDEO|>",
+  "vision_bos_token": "<|vision_bos|>",
+  "vision_eos_token": "<|vision_eos|>"
+}

simuls2st_adapter/talker_components.pt

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+version https://git-lfs.github.com/spec/v1
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+size 834052562

simuls2st_adapter/talker_lora/adapter_config.json

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+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen2.5-Omni-3B/talker",
+  "bias": "none",
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+  "ensure_weight_tying": false,
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+  "init_lora_weights": true,
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+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
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+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
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+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "up_proj",
+    "v_proj",
+    "k_proj",
+    "gate_proj",
+    "down_proj",
+    "thinker_to_talker_proj",
+    "q_proj",
+    "o_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "FEATURE_EXTRACTION",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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simuls2st_adapter/thinker_lora/adapter_config.json

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+{
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+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen2.5-Omni-3B/thinker",
+  "bias": "none",
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+  "use_dora": false,
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+  "use_rslora": false
+}

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simuls2st_adapter/tokenizer_config.json

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+    "image_token": "<|IMAGE|>",
+    "video_token": "<|VIDEO|>",
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+  "video_token": "<|VIDEO|>",
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+}

simuls2st_config.json

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+{
+  "format_version": 1,
+  "model_type": "simuls2st-omni",
+  "offline_model": "offline",
+  "simuls2st_adapter": "simuls2st_adapter",
+  "voicebox": "voicebox/voicebox.safetensors",
+  "vocos": "voicebox/vocos.safetensors",
+  "voicebox_config": "voicebox/voicebox_config.json",
+  "dualcodec": "dualcodec/dualcodec.safetensors",
+  "dualcodec_stats": "dualcodec/w2v_bert_stats.pt",
+  "w2v": "w2v",
+  "sample_rates": {
+    "input": 16000,
+    "output": 24000
+  }
+}

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voicebox/voicebox.safetensors

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+size 1384358160

voicebox/voicebox_config.json

@@ -0,0 +1,59 @@
+{
+    "preprocess": {
+        "tokenizer_path": "checkpoints",
+        "min_dur": 0.01,
+        "max_dur": 30,
+        "pad_token_id": 151643,
+        "end_token_id": 151645,
+        "bos_audio_token_id": 151683,
+        "eos_audio_token_id": 151684,
+        "audio_token_shift": 151685,
+        "hop_size": 480,
+        "sample_rate": 24000,
+        "n_fft": 1920,
+        "num_mels": 128,
+        "win_size": 1920,
+        "fmin": 0,
+        "fmax": 12000,
+        "mel_var": 8.14,
+        "mel_mean": -4.92
+    },
+    "model": {
+        "pretrained_model_path": "checkpoints",
+        "voicebox": {
+            "mel_dim": 128,
+            "hidden_size": 1024,
+            "num_layers": 16,
+            "num_heads": 16,
+            "cfg_scale": 0.2,
+            "use_cond_code": true,
+            "cond_codebook_size": 16384,
+            "cond_dim": 1024,
+            "cond_scale_factor": 4,
+            "use_pretrained_model": false,
+            "sigma": 1e-5,
+            "time_scheduler": "cos"
+        },
+        "kmeans": {
+            "type": "dual_codec",
+            "stat_mean_var_path": "dualcodec/w2v_bert_stats.pt"
+        },
+        "dual_codec": {
+            "n_codebooks": 7,
+            "codebook_size": 4096,
+            "semantic_codebook_size": 16384,
+            "is_causal": true,
+            "semantic_downsample_factor": 4,
+            "pretrained_path": "dualcodec/dualcodec.safetensors"
+        },
+        "vocos": {
+            "input_channels": 128,
+            "dim": 1024,
+            "intermediate_dim": 4096,
+            "num_layers": 30,
+            "n_fft": 1920,
+            "hop_size": 480,
+            "padding": "same"
+        }
+    }
+}

w2v/UPSTREAM_README.md

@@ -0,0 +1,184 @@
+---
+license: mit
+language:
+- af
+- am
+- ar
+- as
+- az
+- be
+- bn
+- bs
+- bg
+- ca
+- cs
+- zh
+- cy
+- da
+- de
+- el
+- en
+- et
+- fi
+- fr
+- or
+- om
+- ga
+- gl
+- gu
+- ha
+- he
+- hi
+- hr
+- hu
+- hy
+- ig
+- id
+- is
+- it
+- jv
+- ja
+- kn
+- ka
+- kk
+- mn
+- km
+- ky
+- ko
+- lo
+- ln
+- lt
+- lb
+- lg
+- lv
+- ml
+- mr
+- mk
+- mt
+- mi
+- my
+- nl
+- nb
+- ne
+- ny
+- oc
+- pa
+- ps
+- fa
+- pl
+- pt
+- ro
+- ru
+- sk
+- sl
+- sn
+- sd
+- so
+- es
+- sr
+- sv
+- sw
+- ta
+- te
+- tg
+- tl
+- th
+- tr
+- uk
+- ur
+- uz
+- vi
+- wo
+- xh
+- yo
+- ms
+- zu
+- ary
+- arz
+- yue
+- kea
+inference: false
+---
+# W2v-BERT 2.0 speech encoder
+
+We are open-sourcing our Conformer-based [W2v-BERT 2.0 speech encoder](#w2v-bert-20-speech-encoder) as described in Section 3.2.1 of the [paper](https://arxiv.org/pdf/2312.05187.pdf), which is at the core of our Seamless models.
+
+This model was pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages. It requires finetuning to be used for downstream tasks such as Automatic Speech Recognition (ASR), or Audio Classification.
+
+| Model Name        | #params | checkpoint                                                                                                                                                                                                                                                                                                                                                                 |
+| ----------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| W2v-BERT 2.0 | 600M    | [checkpoint](https://huggingface.co/reach-vb/conformer-shaw/resolve/main/conformer_shaw.pt)
+
+**This model and its training are supported by 🤗 Transformers, more on it in the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/wav2vec2-bert).**
+
+
+# 🤗 Transformers usage
+
+This is a bare checkpoint without any modeling head, and thus requires finetuning to be used for downstream tasks such as ASR. You can however use it to extract audio embeddings from the top layer with this code snippet:
+
+```python
+from transformers import AutoFeatureExtractor, Wav2Vec2BertModel
+import torch
+from datasets import load_dataset
+
+dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
+dataset = dataset.sort("id")
+sampling_rate = dataset.features["audio"].sampling_rate
+
+processor = AutoProcessor.from_pretrained("facebook/w2v-bert-2.0")
+model = Wav2Vec2BertModel.from_pretrained("facebook/w2v-bert-2.0")
+
+# audio file is decoded on the fly
+inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+```
+
+To learn more about the model use, refer to the following resources:
+- [its docs](https://huggingface.co/docs/transformers/main/en/model_doc/wav2vec2-bert)
+- [a blog post showing how to fine-tune it on Mongolian ASR](https://huggingface.co/blog/fine-tune-w2v2-bert)
+- [a training script example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py)
+
+
+# Seamless Communication usage
+
+This model can be used in [Seamless Communication](https://github.com/facebookresearch/seamless_communication), where it was released.
+ 
+Here's how to make a forward pass through the voice encoder, after having completed the [installation steps](https://github.com/facebookresearch/seamless_communication?tab=readme-ov-file#installation):
+
+```python
+import torch
+
+from fairseq2.data.audio import AudioDecoder, WaveformToFbankConverter
+from fairseq2.memory import MemoryBlock
+from fairseq2.nn.padding import get_seqs_and_padding_mask
+from pathlib import Path
+from seamless_communication.models.conformer_shaw import load_conformer_shaw_model
+
+
+audio_wav_path, device, dtype = ...
+audio_decoder = AudioDecoder(dtype=torch.float32, device=device)
+fbank_converter = WaveformToFbankConverter(
+    num_mel_bins=80,
+    waveform_scale=2**15,
+    channel_last=True,
+    standardize=True,
+    device=device,
+    dtype=dtype,
+)
+collater = Collater(pad_value=1)
+
+model = load_conformer_shaw_model("conformer_shaw", device=device, dtype=dtype)
+model.eval()
+
+with Path(audio_wav_path).open("rb") as fb:
+    block = MemoryBlock(fb.read())
+
+decoded_audio = audio_decoder(block)
+src = collater(fbank_converter(decoded_audio))["fbank"]
+seqs, padding_mask = get_seqs_and_padding_mask(src)
+
+with torch.inference_mode():
+  seqs, padding_mask = model.encoder_frontend(seqs, padding_mask)
+  seqs, padding_mask = model.encoder(seqs, padding_mask)
+```

w2v/config.json

@@ -0,0 +1,81 @@
+{
+  "activation_dropout": 0.0,
+  "adapter_act": "relu",
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": false,
+  "architectures": [
+    "Wav2Vec2BertModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "classifier_proj_size": 768,
+  "codevector_dim": 768,
+  "conformer_conv_dropout": 0.1,
+  "contrastive_logits_temperature": 0.1,
+  "conv_depthwise_kernel_size": 31,
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "eos_token_id": 2,
+  "feat_proj_dropout": 0.0,
+  "feat_quantizer_dropout": 0.0,
+  "feature_projection_input_dim": 160,
+  "final_dropout": 0.1,
+  "hidden_act": "swish",
+  "hidden_dropout": 0.0,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.1,
+  "left_max_position_embeddings": 64,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_source_positions": 5000,
+  "model_type": "wav2vec2-bert",
+  "num_adapter_layers": 1,
+  "num_attention_heads": 16,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_hidden_layers": 24,
+  "num_negatives": 100,
+  "output_hidden_size": 1024,
+  "pad_token_id": 0,
+  "position_embeddings_type": "relative_key",
+  "proj_codevector_dim": 768,
+  "right_max_position_embeddings": 8,
+  "rotary_embedding_base": 10000,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.0.dev0",
+  "use_intermediate_ffn_before_adapter": false,
+  "use_weighted_layer_sum": false,
+  "vocab_size": null,
+  "xvector_output_dim": 512
+}

w2v/model.safetensors

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w2v/preprocessor_config.json

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+{
+  "feature_extractor_type": "SeamlessM4TFeatureExtractor",
+  "feature_size": 80,
+  "num_mel_bins": 80,
+  "padding_side": "right",
+  "padding_value": 1,
+  "processor_class": "Wav2Vec2BertProcessor",
+  "return_attention_mask": true,
+  "sampling_rate": 16000,
+  "stride": 2
+}