Create text_to_audio.py

text_to_audio.py

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+from transformers import pipeline
+from datasets import load_dataset
+import soundfile as sf
+import torch
+
+synthesiser = pipeline("text-to-speech", "microsoft/speecht5_tts")
+
+def text_to_audio(text):
+    # clean the response and max_size is 600
+    text_clean = text.replace('\n', '').replace('*', '')
+    text_550 = text_clean[:590]
+    # get speaker embeddings
+    embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+    speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+    # You can replace this embedding with your own as well.
+
+    speech = synthesiser(text_550, forward_params={"speaker_embeddings": speaker_embedding})
+    sf.write("output.wav", speech["audio"], samplerate=speech["sampling_rate"])
+    audio_file = open("output.wav", "rb")
+    audio_bytes = audio_file.read()
+    return audio_bytes