Instructions to use lithish2602/speecht5_tts_ta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lithish2602/speecht5_tts_ta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="lithish2602/speecht5_tts_ta")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("lithish2602/speecht5_tts_ta") model = AutoModelForTextToSpectrogram.from_pretrained("lithish2602/speecht5_tts_ta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +5 -17
- config.json +1 -1
- generation_config.json +1 -1
- model.safetensors +1 -1
- runs/Feb11_10-22-02_4b6f221b5896/events.out.tfevents.1739269327.4b6f221b5896.1368.0 +3 -0
- training_args.bin +1 -1
README.md
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# speecht5_tts_ta
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_17_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5678
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## Model description
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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- optimizer: Use
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- lr_scheduler_type: linear
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- training_steps: 4000
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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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| 0.3551 | 500.0 | 1000 | 0.4988 |
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| 0.3218 | 1000.0 | 2000 | 0.5528 |
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| 0.2891 | 1500.0 | 3000 | 0.5593 |
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| 0.282 | 2000.0 | 4000 | 0.5678 |
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### Framework versions
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- Transformers 4.
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- Pytorch 2.5.
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- Datasets 3.
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- Tokenizers 0.
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# speecht5_tts_ta
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_17_0 dataset.
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## Model description
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- training_steps: 4000
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.49.0.dev0
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- Pytorch 2.5.1+cu124
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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config.json
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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.
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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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"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": "4.49.0.dev0",
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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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generation_config.json
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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.
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}
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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": "4.49.0.dev0"
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}
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model.safetensors
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runs/Feb11_10-22-02_4b6f221b5896/events.out.tfevents.1739269327.4b6f221b5896.1368.0
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training_args.bin
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