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
CHANGED
|
@@ -18,7 +18,7 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 18 |
|
| 19 |
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_17_0 dataset.
|
| 20 |
It achieves the following results on the evaluation set:
|
| 21 |
-
- Loss: 0.
|
| 22 |
|
| 23 |
## Model description
|
| 24 |
|
|
@@ -52,15 +52,15 @@ The following hyperparameters were used during training:
|
|
| 52 |
|
| 53 |
| Training Loss | Epoch | Step | Validation Loss |
|
| 54 |
|:-------------:|:------:|:----:|:---------------:|
|
| 55 |
-
| 0.
|
| 56 |
-
| 0.
|
| 57 |
-
| 0.
|
| 58 |
-
| 0.
|
| 59 |
|
| 60 |
|
| 61 |
### Framework versions
|
| 62 |
|
| 63 |
-
- Transformers 4.
|
| 64 |
- Pytorch 2.5.1+cu124
|
| 65 |
-
- Datasets 3.
|
| 66 |
- Tokenizers 0.21.0
|
|
|
|
| 18 |
|
| 19 |
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_17_0 dataset.
|
| 20 |
It achieves the following results on the evaluation set:
|
| 21 |
+
- Loss: 0.6856
|
| 22 |
|
| 23 |
## Model description
|
| 24 |
|
|
|
|
| 52 |
|
| 53 |
| Training Loss | Epoch | Step | Validation Loss |
|
| 54 |
|:-------------:|:------:|:----:|:---------------:|
|
| 55 |
+
| 0.3035 | 500.0 | 1000 | 0.6257 |
|
| 56 |
+
| 0.2769 | 1000.0 | 2000 | 0.6513 |
|
| 57 |
+
| 0.2608 | 1500.0 | 3000 | 0.6888 |
|
| 58 |
+
| 0.2503 | 2000.0 | 4000 | 0.6856 |
|
| 59 |
|
| 60 |
|
| 61 |
### Framework versions
|
| 62 |
|
| 63 |
+
- Transformers 4.50.0.dev0
|
| 64 |
- Pytorch 2.5.1+cu124
|
| 65 |
+
- Datasets 3.0.1
|
| 66 |
- Tokenizers 0.21.0
|