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--- |
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language: |
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- sv |
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license: mit |
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tags: |
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- common_voice |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_13_0 |
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pipeline_tag: text-to-speech |
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inference: false |
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base_model: microsoft/speecht5_tts |
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model-index: |
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- name: SpeechT5 TTS Swedish |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# SpeechT5 TTS Swedish |
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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 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4621 |
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## Model description |
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Swedish SpeechT5 model trained on Swedish language in Common Voice. Example on how to implement the model below. Test the model yourself at [https://huggingface.co/spaces/GreenCounsel/SpeechT5-sv](https://huggingface.co/spaces/GreenCounsel/SpeechT5-sv) (not possible to run pipeline inference at Huggingface). |
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``` |
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#pip install datasets soundfile |
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#pip install transformers |
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#pip install sentencepiece |
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from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, set_seed |
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import torch |
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processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") |
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model = SpeechT5ForTextToSpeech.from_pretrained("GreenCounsel/speecht5_tts_common_voice_5_sv") |
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") |
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repl = [ |
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('Ä', 'ae'), |
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('Å', 'o'), |
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('Ö', 'oe'), |
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('ä', 'ae'), |
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('å', 'o'), |
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('ö', 'oe'), |
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('ô','oe'), |
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('-',''), |
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('‘',''), |
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('’',''), |
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('“',''), |
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('”',''), |
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] |
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from datasets import load_dataset |
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embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") |
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speaker_embeddings = torch.tensor(embeddings_dataset[7000]["xvector"]).unsqueeze(0) |
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set_seed(555) |
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text="Förstår du vad han menar?" |
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for src, dst in repl: |
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text = text.replace(src, dst) |
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inputs = processor(text=text, return_tensors="pt") |
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speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder) |
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import soundfile as sf |
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sf.write("output.wav", speech.numpy(), samplerate=16000) |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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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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| 0.5349 | 4.8 | 1000 | 0.4953 | |
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| 0.5053 | 9.59 | 2000 | 0.4714 | |
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| 0.5032 | 14.39 | 3000 | 0.4646 | |
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| 0.4958 | 19.18 | 4000 | 0.4621 | |
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### Framework versions |
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- Transformers 4.30.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
