---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- fleurs
model-index:
- name: speecht5_finetuned_fleurs_zh_4000
  results: []
---

<!-- 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. -->

# speecht5_finetuned_fleurs_zh_4000

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3888

## Model description

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: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7366        | 1.09  | 100  | 0.6059          |
| 0.5892        | 2.19  | 200  | 0.5104          |
| 0.5436        | 3.28  | 300  | 0.4585          |
| 0.4848        | 4.38  | 400  | 0.4333          |
| 0.4733        | 5.47  | 500  | 0.4276          |
| 0.4534        | 6.57  | 600  | 0.4194          |
| 0.454         | 7.66  | 700  | 0.4172          |
| 0.4489        | 8.76  | 800  | 0.4111          |
| 0.4401        | 9.85  | 900  | 0.4108          |
| 0.441         | 10.94 | 1000 | 0.4136          |
| 0.437         | 12.04 | 1100 | 0.4078          |
| 0.4333        | 13.13 | 1200 | 0.4067          |
| 0.4328        | 14.23 | 1300 | 0.4002          |
| 0.4289        | 15.32 | 1400 | 0.4015          |
| 0.4254        | 16.42 | 1500 | 0.4012          |
| 0.427         | 17.51 | 1600 | 0.4020          |
| 0.4273        | 18.6  | 1700 | 0.4008          |
| 0.4222        | 19.7  | 1800 | 0.3966          |
| 0.4305        | 20.79 | 1900 | 0.3998          |
| 0.4198        | 21.89 | 2000 | 0.3954          |
| 0.4225        | 22.98 | 2100 | 0.3961          |
| 0.4223        | 24.08 | 2200 | 0.3965          |
| 0.4201        | 25.17 | 2300 | 0.3922          |
| 0.4234        | 26.27 | 2400 | 0.3939          |
| 0.4213        | 27.36 | 2500 | 0.3930          |
| 0.4182        | 28.45 | 2600 | 0.3934          |
| 0.4119        | 29.55 | 2700 | 0.3925          |
| 0.4113        | 30.64 | 2800 | 0.3907          |
| 0.4131        | 31.74 | 2900 | 0.3907          |
| 0.4135        | 32.83 | 3000 | 0.3933          |
| 0.4142        | 33.93 | 3100 | 0.3909          |
| 0.4144        | 35.02 | 3200 | 0.3919          |
| 0.414         | 36.11 | 3300 | 0.3919          |
| 0.418         | 37.21 | 3400 | 0.3899          |
| 0.4094        | 38.3  | 3500 | 0.3897          |
| 0.4149        | 39.4  | 3600 | 0.3924          |
| 0.4105        | 40.49 | 3700 | 0.3905          |
| 0.413         | 41.59 | 3800 | 0.3895          |
| 0.4117        | 42.68 | 3900 | 0.3900          |
| 0.4096        | 43.78 | 4000 | 0.3888          |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3