--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: speecht5_finetuned_speaking_style_en_2 results: [] --- # speecht5_finetuned_speaking_style_en_2 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3193 ## 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: 8 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.4074 | 0.61 | 100 | 0.3635 | | 0.4029 | 1.23 | 200 | 0.3629 | | 0.4041 | 1.84 | 300 | 0.3617 | | 0.4006 | 2.45 | 400 | 0.3605 | | 0.3987 | 3.07 | 500 | 0.3563 | | 0.3983 | 3.68 | 600 | 0.3557 | | 0.3949 | 4.29 | 700 | 0.3529 | | 0.388 | 4.9 | 800 | 0.3515 | | 0.3842 | 5.52 | 900 | 0.3484 | | 0.3833 | 6.13 | 1000 | 0.3484 | | 0.3789 | 6.74 | 1100 | 0.3468 | | 0.378 | 7.36 | 1200 | 0.3431 | | 0.3737 | 7.97 | 1300 | 0.3432 | | 0.3737 | 8.58 | 1400 | 0.3432 | | 0.3722 | 9.2 | 1500 | 0.3429 | | 0.3702 | 9.81 | 1600 | 0.3391 | | 0.3672 | 10.42 | 1700 | 0.3373 | | 0.3657 | 11.03 | 1800 | 0.3376 | | 0.3612 | 11.65 | 1900 | 0.3377 | | 0.3615 | 12.26 | 2000 | 0.3327 | | 0.3597 | 12.87 | 2100 | 0.3326 | | 0.358 | 13.49 | 2200 | 0.3317 | | 0.3542 | 14.1 | 2300 | 0.3348 | | 0.3559 | 14.71 | 2400 | 0.3310 | | 0.3567 | 15.33 | 2500 | 0.3335 | | 0.3541 | 15.94 | 2600 | 0.3333 | | 0.3524 | 16.55 | 2700 | 0.3298 | | 0.3494 | 17.16 | 2800 | 0.3287 | | 0.3508 | 17.78 | 2900 | 0.3260 | | 0.3487 | 18.39 | 3000 | 0.3274 | | 0.3484 | 19.0 | 3100 | 0.3295 | | 0.3472 | 19.62 | 3200 | 0.3263 | | 0.3469 | 20.23 | 3300 | 0.3263 | | 0.3454 | 20.84 | 3400 | 0.3280 | | 0.3431 | 21.46 | 3500 | 0.3286 | | 0.3444 | 22.07 | 3600 | 0.3275 | | 0.3435 | 22.68 | 3700 | 0.3281 | | 0.345 | 23.3 | 3800 | 0.3247 | | 0.3438 | 23.91 | 3900 | 0.3263 | | 0.3404 | 24.52 | 4000 | 0.3256 | | 0.342 | 25.13 | 4100 | 0.3273 | | 0.3419 | 25.75 | 4200 | 0.3226 | | 0.34 | 26.36 | 4300 | 0.3218 | | 0.3404 | 26.97 | 4400 | 0.3266 | | 0.3401 | 27.59 | 4500 | 0.3222 | | 0.3398 | 28.2 | 4600 | 0.3236 | | 0.3393 | 28.81 | 4700 | 0.3237 | | 0.3377 | 29.43 | 4800 | 0.3225 | | 0.3374 | 30.04 | 4900 | 0.3236 | | 0.3376 | 30.65 | 5000 | 0.3216 | | 0.3352 | 31.26 | 5100 | 0.3230 | | 0.3367 | 31.88 | 5200 | 0.3208 | | 0.3368 | 32.49 | 5300 | 0.3247 | | 0.3367 | 33.1 | 5400 | 0.3226 | | 0.3375 | 33.72 | 5500 | 0.3203 | | 0.3365 | 34.33 | 5600 | 0.3209 | | 0.3353 | 34.94 | 5700 | 0.3231 | | 0.3352 | 35.56 | 5800 | 0.3201 | | 0.3335 | 36.17 | 5900 | 0.3209 | | 0.334 | 36.78 | 6000 | 0.3204 | | 0.3342 | 37.39 | 6100 | 0.3203 | | 0.3327 | 38.01 | 6200 | 0.3195 | | 0.3342 | 38.62 | 6300 | 0.3196 | | 0.3325 | 39.23 | 6400 | 0.3214 | | 0.3321 | 39.85 | 6500 | 0.3190 | | 0.3326 | 40.46 | 6600 | 0.3191 | | 0.3323 | 41.07 | 6700 | 0.3215 | | 0.3325 | 41.69 | 6800 | 0.3197 | | 0.3325 | 42.3 | 6900 | 0.3198 | | 0.3315 | 42.91 | 7000 | 0.3194 | | 0.3317 | 43.52 | 7100 | 0.3196 | | 0.3326 | 44.14 | 7200 | 0.3234 | | 0.3304 | 44.75 | 7300 | 0.3196 | | 0.3308 | 45.36 | 7400 | 0.3207 | | 0.3313 | 45.98 | 7500 | 0.3182 | | 0.3308 | 46.59 | 7600 | 0.3182 | | 0.3305 | 47.2 | 7700 | 0.3188 | | 0.3308 | 47.82 | 7800 | 0.3193 | | 0.3313 | 48.43 | 7900 | 0.3199 | | 0.3306 | 49.04 | 8000 | 0.3201 | | 0.3307 | 49.66 | 8100 | 0.3187 | | 0.3295 | 50.27 | 8200 | 0.3185 | | 0.3298 | 50.88 | 8300 | 0.3190 | | 0.3301 | 51.49 | 8400 | 0.3205 | | 0.3299 | 52.11 | 8500 | 0.3202 | | 0.3297 | 52.72 | 8600 | 0.3212 | | 0.3302 | 53.33 | 8700 | 0.3206 | | 0.3288 | 53.95 | 8800 | 0.3192 | | 0.3286 | 54.56 | 8900 | 0.3189 | | 0.3287 | 55.17 | 9000 | 0.3193 | | 0.3302 | 55.79 | 9100 | 0.3191 | | 0.328 | 56.4 | 9200 | 0.3196 | | 0.3292 | 57.01 | 9300 | 0.3188 | | 0.3288 | 57.62 | 9400 | 0.3175 | | 0.3274 | 58.24 | 9500 | 0.3194 | | 0.3289 | 58.85 | 9600 | 0.3191 | | 0.3287 | 59.46 | 9700 | 0.3179 | | 0.3293 | 60.08 | 9800 | 0.3208 | | 0.3279 | 60.69 | 9900 | 0.3199 | | 0.3282 | 61.3 | 10000 | 0.3193 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2