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--- |
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library_name: transformers |
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license: mit |
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base_model: fahadqazi/Sindhi-TTS |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Sindhi-TTS |
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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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# Sindhi-TTS |
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This model is a fine-tuned version of [fahadqazi/Sindhi-TTS](https://huggingface.co/fahadqazi/Sindhi-TTS) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.4602 |
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- eval_runtime: 47.8291 |
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- eval_samples_per_second: 36.421 |
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- eval_steps_per_second: 18.211 |
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- epoch: 13.2653 |
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- step: 6500 |
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## How to use |
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``` |
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from transformers import SpeechT5ForTextToSpeech, SpeechT5ForSpeechToText |
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from transformers import SpeechT5Processor |
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from transformers import AutoTokenizer |
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from transformers import SpeechT5HifiGan |
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import torch |
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from IPython.display import Audio as IPythonAudio |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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# imporing speech processor from another repo |
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processor = SpeechT5Processor.from_pretrained("Sana1207/Hindi_SpeechT5_finetuned") |
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# importing tokenizer and assigning it to the speech processor |
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tokenizer = AutoTokenizer.from_pretrained("fahadqazi/Sindhi-TTS") |
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processor.tokenizer = tokenizer |
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# importing the model |
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model = SpeechT5ForTextToSpeech.from_pretrained("fahadqazi/Sindhi-TTS") |
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# importing the vocoder from microsoft's repository |
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device) |
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# loading random vocodings (the voice) |
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embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") |
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speaker_embeddings = embeddings_dataset[7306]["xvector"] |
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speaker_embeddings = torch.tensor(speaker_embeddings).to(device).unsqueeze(0) |
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# Generating Speech |
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text = "ڪهڙا حال آهن" |
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inputs = processor(text=text, return_tensors="pt").to(device) |
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speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder) |
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IPythonAudio(speech.cpu().numpy(), rate=16000, autoplay=True) |
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``` |
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## Model description |
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More information needed |
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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: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 2 |
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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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 200 |
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- training_steps: 10000 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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