Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+title: OpenF5 TTS
+sdk: gradio
+emoji: 🔥
+short_description: Demo for OpenF5-TTS
+sdk_version: 5.26.0
+colorFrom: yellow
+colorTo: yellow
+pinned: true
+---

app.py

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+import spaces
+import tempfile
+import gradio as gr
+from f5_tts.api import F5TTS
+from huggingface_hub import hf_hub_download
+import os
+
+VARIANTS = ["model_850000", "model_300000", "model_100000"]
+models = {}
+for variant in VARIANTS:
+    models[variant] = F5TTS(
+        ckpt_file=hf_hub_download("mrfakename/openf5-v2", f"{variant}.pt", token=os.getenv("HF_TOKEN")),
+        vocab_file=hf_hub_download("mrfakename/openf5-v2", "vocab.txt", token=os.getenv("HF_TOKEN"))
+    )
+
+@spaces.GPU
+def generate(text, ref_audio, variant, progress = gr.Progress()):
+    api = models[variant]
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+        api.infer(
+            ref_file=ref_audio,
+            ref_text="",
+            gen_text=text,
+            progress=progress,
+            file_wave=f.name
+        )
+    print(f.name)
+    return f.name
+
+with gr.Blocks() as demo:
+    gr.Markdown("# OpenF5 TTS Demo\n\nTry out various checkpoints of the OpenF5-TTS model. The model is currently still being trained.\n\nThe goal is to create a permissively-licensed F5-TTS model checkpoint trained on commercially-viable data that can be used as a base for further fine-tuning.")
+    textbox = gr.Textbox(label="Text")
+    audio = gr.Audio(label="Reference Audio", type="filepath")
+    variant = gr.Radio(choices=VARIANTS, value=VARIANTS[0], label="Variant")
+    btn = gr.Button("Generate", variant="primary")
+    output = gr.Audio(label="Output", type="filepath")
+    btn.click(generate, [textbox, audio, variant], outputs=[output])
+
+demo.queue().launch()

requirements.txt

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+f5-tts