clap-htsat-fused-librispeech

CLAP model trained on COCO Captions

This is a sentence-transformers model finetuned from laion/clap-htsat-fused on the librispeech_asr dataset. It maps sentences & paragraphs to a None-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: laion/clap-htsat-fused <!-- at revision 1d58d5192f5e4f16b57c574c7daf3d941404bd06 -->
• Maximum Sequence Length: None tokens
• Output Dimensionality: None dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • librispeech_asr
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'get_text_features', 'method_output_name': None}, 'audio': {'method': 'get_audio_features', 'method_output_name': None}}, 'module_output_name': 'sentence_embedding', 'architecture': 'ClapModel'})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/clap-htsat-fused-librispeech")
# Run inference
sentences = [
    'THERE ARE NATURES TOO TO WHOSE SENSE OF JUSTICE THE PRICE EXACTED LOOMS UP MONSTROUSLY ENORMOUS ODIOUS OPPRESSIVE WORRYING HUMILIATING EXTORTIONATE INTOLERABLE THOSE ARE THE FANATICS',
    'HE BEGAN TO WISH THAT HE HAD COMPROMISED IN SOME WAY OR OTHER THAT HE HAD SENT THE MONEY PERHAPS HE COULD DO IT UP HERE',
    'HERE THE HOLY PRELATE OF FERNS MET HIM AND RELATED A VISION IN WHICH HE HAD BEEN INSTRUCTED TO DEMAND THE ABOLITION OF THE IMPOST',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, -0.4742, -0.2719],
#         [-0.4742,  1.0000,  0.8206],
#         [-0.2719,  0.8206,  1.0000]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Information Retrieval

• Datasets: librispeech-eval and librispeech-test
• Evaluated with <code>InformationRetrievalEvaluator</code>

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

librispeech_asr

• Dataset: librispeech_asr at 71cacbf
• Size: 132,553 training samples
• Columns: <code>audio</code> and <code>text</code>
• Approximate statistics based on the first 1000 samples:

  	audio	text
  type	dict	string
  details	<ul><li></li></ul>	<ul><li>min: 20 characters</li><li>mean: 189.15 characters</li><li>max: 294 characters</li></ul>

• Samples:

  audio	text
  <code>{'path': '374-180298-0000.flac', 'array': array([ 6.92203816e-04, 8.04404495e-04, 8.03834875e-04, ...,<br> -3.02505396e-05, -6.59527450e-06, 1.11444592e-06]), 'sampling_rate': 48000}</code>	<code>CHAPTER SIXTEEN I MIGHT HAVE TOLD YOU OF THE BEGINNING OF THIS LIAISON IN A FEW LINES BUT I WANTED YOU TO SEE EVERY STEP BY WHICH WE CAME I TO AGREE TO WHATEVER MARGUERITE WISHED</code>
  <code>{'path': '374-180298-0001.flac', 'array': array([-9.33515839e-05, -1.25754057e-04, -1.44482241e-04, ...,<br> -2.66165182e-04, -2.03228556e-04, -1.03404833e-04]), 'sampling_rate': 48000}</code>	<code>MARGUERITE TO BE UNABLE TO LIVE APART FROM ME IT WAS THE DAY AFTER THE EVENING WHEN SHE CAME TO SEE ME THAT I SENT HER MANON LESCAUT FROM THAT TIME SEEING THAT I COULD NOT CHANGE MY MISTRESS'S LIFE I CHANGED MY OWN</code>
  <code>{'path': '374-180298-0002.flac', 'array': array([-2.47883319e-04, -2.91854434e-04, -2.82971043e-04, ...,<br> -1.43931946e-04, -1.17829914e-04, -6.32331648e-05]), 'sampling_rate': 48000}</code>	<code>I WISHED ABOVE ALL NOT TO LEAVE MYSELF TIME TO THINK OVER THE POSITION I HAD ACCEPTED FOR IN SPITE OF MYSELF IT WAS A GREAT DISTRESS TO ME THUS MY LIFE GENERALLY SO CALM</code>

• Loss: <code>MultipleNegativesSymmetricRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Evaluation Dataset

librispeech_asr

• Dataset: librispeech_asr at 71cacbf
• Size: 1,000 evaluation samples
• Columns: <code>audio</code> and <code>text</code>
• Approximate statistics based on the first 1000 samples:

  	audio	text
  type	dict	string
  details	<ul><li></li></ul>	<ul><li>min: 8 characters</li><li>mean: 104.62 characters</li><li>max: 516 characters</li></ul>

• Samples:

  audio	text
  <code>{'path': '2277-149896-0000.flac', 'array': array([ 0.00179741, 0.00170625, 0.00120927, ..., -0.00144462,<br> -0.00102732, -0.00048062]), 'sampling_rate': 48000}</code>	<code>HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE</code>
  <code>{'path': '2277-149896-0001.flac', 'array': array([ 0.00111104, 0.00081758, 0.00021103, ..., -0.00138193,<br> -0.0009173 , -0.00041702]), 'sampling_rate': 48000}</code>	<code>HE WOULD HAVE TO PAY HER THE MONEY WHICH SHE WOULD NOW REGULARLY DEMAND OR THERE WOULD BE TROUBLE IT DID NOT MATTER WHAT HE DID</code>
  <code>{'path': '2277-149896-0002.flac', 'array': array([0.00080266, 0.00088462, 0.00083408, ..., 0.00105488, 0.00083673,<br> 0.00043296]), 'sampling_rate': 48000}</code>	<code>HURSTWOOD WALKED THE FLOOR MENTALLY ARRANGING THE CHIEF POINTS OF HIS SITUATION</code>

• Loss: <code>MultipleNegativesSymmetricRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• bf16: True
• batch_sampler: no_duplicates

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• use_cpu: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• bf16: True
• fp16: False
• half_precision_backend: None
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• hub_revision: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_for_metrics: []
• eval_do_concat_batches: True
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: no
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: True
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

<details><summary>Click to expand</summary> </details>

Environmental Impact

Carbon emissions were measured using CodeCarbon.

• Energy Consumed: 0.429 kWh
• Carbon Emitted: 0.115 kg of CO2
• Hours Used: 2.094 hours

Training Hardware

• On Cloud: No
• GPU Model: 1 x NVIDIA GeForce RTX 3090
• CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
• RAM Size: 31.78 GB

Framework Versions

• Python: 3.11.6
• Sentence Transformers: 5.2.0.dev0
• Transformers: 4.57.0.dev0
• PyTorch: 2.8.0+cu128
• Accelerate: 1.6.0
• Datasets: 3.6.0
• Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

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