Model save

README.md

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-This directory includes a few sample datasets to get you started.
+---
+library_name: transformers
+license: mit
+base_model: microsoft/speecht5_tts
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_17_0
+model-index:
+- name: sample_data
+  results: []
+---
 
-*   `california_housing_data*.csv` is California housing data from the 1990 US
-    Census; more information is available at:
-    https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pub
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-*   `mnist_*.csv` is a small sample of the
-    [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
-    described at: http://yann.lecun.com/exdb/mnist/
+# sample_data
 
-*   `anscombe.json` contains a copy of
-    [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
-    was originally described in
+This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_17_0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6927
 
-    Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
-    Statistician. 27 (1): 17-21. JSTOR 2682899.
+## Model description
 
-    and our copy was prepared by the
-    [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- training_steps: 4000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.3536        | 500.0  | 1000 | 0.6276          |
+| 0.3171        | 1000.0 | 2000 | 0.6680          |
+| 0.2862        | 1500.0 | 3000 | 0.6698          |
+| 0.2792        | 2000.0 | 4000 | 0.6927          |
+
+
+### Framework versions
+
+- Transformers 4.52.0.dev0
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.0
+- Tokenizers 0.21.1

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "decoder_start_token_id": 2,
+  "eos_token_id": 2,
+  "max_length": 1876,
+  "pad_token_id": 1,
+  "transformers_version": "4.52.0.dev0"
+}