Laurent
commited on
Commit
·
45e7a42
1
Parent(s):
a2dc69b
Revert "transformers-integration (#2)"
Browse filesThis reverts commit a2dc69b6c6d5e96a88da602d110dbd1a137d17a0.
- README.md +0 -73
- config.json +1 -76
- generation_config.json +0 -9
- preprocessor_config.json +0 -13
- special_tokens_map.json +0 -3
- tokenizer.json +0 -0
- tokenizer_config.json +0 -50
- transformers-00001-of-00002.safetensors +0 -3
- transformers-00002-of-00002.safetensors +0 -3
- transformers.safetensors.index.json +0 -745
README.md
CHANGED
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@@ -50,79 +50,6 @@ The predicted text token timestamps can be recovered by subtracting the model's
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See the [GitHub repository](https://github.com/kyutai-labs/delayed-streams-modeling/).
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### Use with transformers
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Install transformers from source:
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```bash
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pip install git+https://github.com/huggingface/transformers
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```
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Inference:
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```python
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import torch
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from datasets import load_dataset, Audio
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from transformers import KyutaiSpeechToTextProcessor, KyutaiSpeechToTextForConditionalGeneration
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# 1. load the model and the processor
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torch_device = "cuda" if torch.cuda.is_available() else "cpu"
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model_id = "kyutai/stt-2.6b-en"
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processor = KyutaiSpeechToTextProcessor.from_pretrained(model_id)
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model = KyutaiSpeechToTextForConditionalGeneration.from_pretrained(model_id, device_map=torch_device)
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# 2. load audio samples
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ds = load_dataset(
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"hf-internal-testing/librispeech_asr_dummy", "clean", split="validation"
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)
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ds = ds.cast_column("audio", Audio(sampling_rate=24000))
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# 3. prepare the model inputs
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inputs = processor(
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ds[0]["audio"]["array"],
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)
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inputs.to(torch_device)
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# 4. infer the model
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output_tokens = model.generate(**inputs)
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# 5. decode the generated tokens
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print(processor.batch_decode(output_tokens, skip_special_tokens=True))
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```
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Batched inference:
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```python
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import torch
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from datasets import load_dataset, Audio
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from transformers import KyutaiSpeechToTextProcessor, KyutaiSpeechToTextForConditionalGeneration
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# 1. load the model and the processor
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torch_device = "cuda" if torch.cuda.is_available() else "cpu"
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model_id = "kyutai/stt-2.6b-en"
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processor = KyutaiSpeechToTextProcessor.from_pretrained(model_id)
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model = KyutaiSpeechToTextForConditionalGeneration.from_pretrained(model_id, device_map=torch_device)
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# 2. load audio samples
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ds = load_dataset(
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"hf-internal-testing/librispeech_asr_dummy", "clean", split="validation"
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)
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ds = ds.cast_column("audio", Audio(sampling_rate=24000))
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# 3. prepare the model inputs
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audio_arrays = [ds[i]["audio"]["array"] for i in range(4)]
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inputs = processor(audio_arrays, return_tensors="pt", padding=True)
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inputs = inputs.to(torch_device)
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# 4. infer the model
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output_tokens = model.generate(**inputs)
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# 5. decode the generated tokens
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decoded_outputs = processor.batch_decode(output_tokens, skip_special_tokens=True)
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for output in decoded_outputs:
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print(output)
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```
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####
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## Training Details
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### Training Data
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See the [GitHub repository](https://github.com/kyutai-labs/delayed-streams-modeling/).
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## Training Details
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### Training Data
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config.json
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@@ -75,80 +75,5 @@
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},
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"model_type": "stt",
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"mimi_name": "[email protected]",
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"tokenizer_name": "tokenizer_en_audio_4000.model"
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"architectures": [
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"KyutaiSpeechToTextForConditionalGeneration"
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],
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"attention_dropout": 0.0,
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"audio_bos_token_id": 2048,
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"audio_pad_token_id": 69569,
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"bos_token_id": 48000,
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"codebook_vocab_size": 2049,
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"codec_config": {
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"attention_bias": false,
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"attention_dropout": 0.0,
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"audio_channels": 1,
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"codebook_dim": 256,
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"codebook_size": 2048,
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"compress": 2,
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"dilation_growth_rate": 2,
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"frame_rate": 12.5,
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"head_dim": 64,
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"hidden_act": "gelu",
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"hidden_size": 512,
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"initializer_range": 0.02,
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"intermediate_size": 2048,
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"kernel_size": 7,
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"last_kernel_size": 3,
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"layer_scale_initial_scale": 0.01,
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"max_position_embeddings": 8000,
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"model_type": "mimi",
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"norm_eps": 1e-05,
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"num_attention_heads": 8,
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"num_filters": 64,
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"num_hidden_layers": 8,
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"num_key_value_heads": 8,
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"num_quantizers": 32,
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"num_residual_layers": 1,
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"num_semantic_quantizers": 1,
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"pad_mode": "constant",
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"residual_kernel_size": 3,
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"rope_theta": 10000.0,
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"sampling_rate": 24000,
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"sliding_window": 250,
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"trim_right_ratio": 1.0,
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"upsample_groups": 512,
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"upsampling_ratios": [
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8,
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6,
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],
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"use_cache": false,
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"use_causal_conv": true,
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"use_conv_shortcut": false,
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"use_streaming": false,
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"vector_quantization_hidden_dimension": 256
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},
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"ffn_dim": 11264,
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"frame_size": 1920,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"max_position_embeddings": 750,
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"num_attention_heads": 32,
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"num_codebooks": 32,
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"num_hidden_layers": 48,
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"num_key_value_heads": 32,
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"pad_token_id": 3,
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"rms_norm_eps": 1e-08,
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"rope_theta": 100000.0,
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"sliding_window": 375,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.53.0.dev0",
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"use_cache": true,
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"vocab_size": 4001,
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"transformers_weights": "transformers.safetensors.index.json"
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}
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},
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"model_type": "stt",
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"mimi_name": "[email protected]",
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"tokenizer_name": "tokenizer_en_audio_4000.model"
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}
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generation_config.json
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{
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"audio_window_size": 1,
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"bos_token_id": 48000,
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"cache_implementation": "sliding_window",
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"codec_cache_implementation": "sliding_window",
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"codec_use_cache": true,
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"pad_token_id": 3,
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"transformers_version": "4.53.0.dev0"
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}
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preprocessor_config.json
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{
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"audio_delay_seconds": 2.5,
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"audio_silence_prefix_seconds": 1.0,
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"chunk_length_s": null,
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"feature_extractor_type": "KyutaiSpeechToTextFeatureExtractor",
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"feature_size": 1,
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"overlap": null,
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"padding_side": "right",
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"padding_value": 0.0,
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"processor_class": "KyutaiSpeechToTextProcessor",
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"return_attention_mask": true,
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"sampling_rate": 24000
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}
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special_tokens_map.json
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{
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"unk_token": "<unk>"
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}
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"bos_token_id": null,
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"chat_template": null,
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"clean_up_tokenization_spaces": false,
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"eos_token_id": null,
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"extra_special_tokens": {},
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 1000000000000000019884624838656,
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"pad_token_id": null,
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"processor_class": "KyutaiSpeechToTextProcessor",
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"tokenizer_class": "PreTrainedTokenizerFast",
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"unk_token": "<unk>"
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}
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transformers-00001-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d10da996924f8718e69ad188d65e4334f6c82a12bcaf8c5df83e80c9f5fca98b
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size 4372392344
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transformers-00002-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:fe01b3882f795cc9198c6e7f1fa4f08000e14f59ac8b181a294535ddb41cc33b
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size 1547899924
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transformers.safetensors.index.json
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{
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"metadata": {
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"total_parameters": 2696431425,
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"total_size": 6759058820
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},
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"weight_map": {
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"codec_model.decoder.layers.0.conv.bias": "transformers-00002-of-00002.safetensors",
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"codec_model.decoder.layers.0.conv.weight": "transformers-00002-of-00002.safetensors",
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