first

app.py

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+import gradio as gr
+import tempfile
+from TTS.utils.synthesizer import Synthesizer
+from huggingface_hub import hf_hub_download
+import torch
+
+CUDA = torch.cuda.is_available()
+
+REPO_ID = "collectivat/catotron-ona"
+
+my_title = "Catotron Text-to-Speech"
+my_description = "This model is based on Fast Speech implemented in 🐸 [Coqui.ai](https://coqui.ai/)." 
+
+my_examples = [
+  ["Catotron, sintesi de la parla obert i lliure en català"],
+  ["Ada Lovelace va ser la primera programadora en la història dels ordinadors."],
+  ["S'espera un dia anticiclònic amb temperatures suaus i vent fluix."]
+]
+
+my_inputs = [
+  gr.Textbox(lines=5, label="Input Text")
+]
+
+my_outputs = gr.Audio(type="filepath", label="Output Audio")
+
+def tts(text: str, split_sentences: bool = True):
+    best_model_path = hf_hub_download(repo_id=REPO_ID, filename="fast-speech_best_model.pth") 
+    config_path = hf_hub_download(repo_id=REPO_ID, filename="fast-speech_config.json")
+    
+    # init synthesizer  
+    synthesizer = Synthesizer(
+        best_model_path,
+        config_path,
+        use_cuda=CUDA
+    )
+
+    # replace oov characters
+    text = text.replace("\n", ". ")
+    text = text.replace("(", ",")
+    text = text.replace(")", ",")
+    text = text.replace(";", ",")
+
+    # create audio file
+    wavs = synthesizer.tts(text, split_sentences=split_sentences)
+    with tempfile.NamedTemporaryFile(suffix = ".wav", delete = False) as fp:
+        synthesizer.save_wav(wavs, fp)                      
+    return fp.name 
+ 
+iface = gr.Interface(
+    fn=tts, 
+    inputs=my_inputs, 
+    outputs=my_outputs, 
+    title=my_title, 
+    description = my_description, 
+    examples = my_examples,
+    cache_examples=True
+)
+iface.launch()

requirements.txt

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+coqui-tts==0.24.3