ft-speech-t5-on-voxpopuli
Browse files- README.md +64 -0
- config.json +92 -0
- generation_config.json +9 -0
- model.safetensors +3 -0
- training_args.bin +3 -0
README.md
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---
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license: mit
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base_model: microsoft/speecht5_tts
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tags:
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- generated_from_trainer
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datasets:
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- fleurs
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model-index:
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- name: ft-speech-t5-on-voxpopuli
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# ft-speech-t5-on-voxpopuli
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the fleurs dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5187
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 4
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 50
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- training_steps: 100
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 0.7238 | 0.84 | 50 | 0.5677 |
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| 0.6067 | 1.68 | 100 | 0.5185 |
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.1.2
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- Datasets 2.15.0
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- Tokenizers 0.15.2
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config.json
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{
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"_name_or_path": "microsoft/speecht5_tts",
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"activation_dropout": 0.1,
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"apply_spec_augment": true,
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"architectures": [
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"SpeechT5ForTextToSpeech"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 0,
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"conv_bias": false,
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"conv_dim": [
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512,
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512,
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512,
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512,
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512,
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512,
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512
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],
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"conv_kernel": [
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10,
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3,
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3,
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3,
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3,
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2,
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2
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],
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"conv_stride": [
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5,
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2,
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2
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],
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"decoder_attention_heads": 12,
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"decoder_ffn_dim": 3072,
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"decoder_layerdrop": 0.1,
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"decoder_layers": 6,
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"decoder_start_token_id": 2,
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"encoder_attention_heads": 12,
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"encoder_ffn_dim": 3072,
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"encoder_layerdrop": 0.1,
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"encoder_layers": 12,
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"encoder_max_relative_position": 160,
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"eos_token_id": 2,
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"feat_extract_activation": "gelu",
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"feat_extract_norm": "group",
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"feat_proj_dropout": 0.0,
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"guided_attention_loss_num_heads": 2,
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"guided_attention_loss_scale": 10.0,
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"guided_attention_loss_sigma": 0.4,
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"hidden_act": "gelu",
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"hidden_dropout": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"is_encoder_decoder": true,
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"layer_norm_eps": 1e-05,
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"mask_feature_length": 10,
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"mask_feature_min_masks": 0,
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"mask_feature_prob": 0.0,
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"mask_time_length": 10,
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"mask_time_min_masks": 2,
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"mask_time_prob": 0.05,
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"max_length": 1876,
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"max_speech_positions": 1876,
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"max_text_positions": 600,
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"model_type": "speecht5",
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"num_conv_pos_embedding_groups": 16,
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"num_conv_pos_embeddings": 128,
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"num_feat_extract_layers": 7,
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"num_mel_bins": 80,
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"pad_token_id": 1,
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"positional_dropout": 0.1,
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"reduction_factor": 2,
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"scale_embedding": false,
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"speaker_embedding_dim": 512,
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"speech_decoder_postnet_dropout": 0.5,
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"speech_decoder_postnet_kernel": 5,
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"speech_decoder_postnet_layers": 5,
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"speech_decoder_postnet_units": 256,
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"speech_decoder_prenet_dropout": 0.5,
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"speech_decoder_prenet_layers": 2,
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"speech_decoder_prenet_units": 256,
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"torch_dtype": "float32",
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"transformers_version": "4.35.2",
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"use_cache": false,
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"use_guided_attention_loss": true,
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"vocab_size": 81
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"decoder_start_token_id": 2,
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"eos_token_id": 2,
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"max_length": 1876,
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"pad_token_id": 1,
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"transformers_version": "4.35.2"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:bdfa4703d63d1b998aa6c15b11006bcb7bcd2c4009e390f16b5212311ca8c34e
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size 577789320
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:95078a0e978afc853d3ccdb10da7f2c856f9de0453062ce33ddc736cb50a54dd
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size 4792
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