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README.md
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inference
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```python
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from transformers import pipeline
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from datasets import load_dataset
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import soundfile as sf
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synthesiser = pipeline("text-to-speech", "umarigan/speecht5_tts_tr_v1.0")
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embeddings_dataset = load_dataset("umarigan/turkish_voice_dataset_embedded", split="train
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speaker_embedding = torch.tensor(embeddings_dataset[736]["speaker_embeddings"]).unsqueeze(0)
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speech = synthesiser("Bir berber bir berbere gel beraber bir berber kuralım demiş", forward_params={"speaker_embeddings": speaker_embedding})
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sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"])
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```
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inference
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```python
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from transformers import pipeline
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from datasets import load_dataset
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import soundfile as sf
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import torch
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from IPython.display import Audio
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synthesiser = pipeline("text-to-speech", "umarigan/speecht5_tts_tr_v1.0")
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embeddings_dataset = load_dataset("umarigan/turkish_voice_dataset_embedded", split="train")
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speaker_embedding = torch.tensor(embeddings_dataset[736]["speaker_embeddings"]).unsqueeze(0)
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# Synthesize speech using the embedding
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speech = synthesiser("Bir berber bir berbere gel beraber bir berber kuralım demiş", forward_params={"speaker_embeddings": speaker_embedding})
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# Save the generated audio to a file
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sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"])
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# Play the audio in the notebook
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Audio("speech.wav")
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```
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