here goes nothing

app.py

@@ -1,7 +1,124 @@
 import gradio as gr
+from fish_speech import LM
+import re
+from rustymimi import Tokenizer
+from huggingface_hub import snapshot_download, hf_hub_download
+import numpy as np
+import spaces
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Voice mapping dictionary:
+# US voices
+#   heart (default) -> <|speaker:0|>
+#   bella -> <|speaker:1|>
+#   nova -> <|speaker:2|>
+#   sky -> <|speaker:3|>
+#   sarah -> <|speaker:4|>
+#   michael -> <|speaker:5|>
+#   fenrir -> <|speaker:6|>
+#   liam -> <|speaker:7|>
+# British voices
+#   emma -> <|speaker:8|>
+#   isabella -> <|speaker:9|>
+#   fable -> <|speaker:10|>
+voice_mapping = {
+    "Heart (US)": "<|speaker:0|>",
+    "Bella (US)": "<|speaker:1|>",
+    "Nova (US)": "<|speaker:2|>",
+    "Sky (US)": "<|speaker:3|>",
+    "Sarah (US)": "<|speaker:4|>",
+    "Michael (US)": "<|speaker:5|>",
+    "Fenrir (US)": "<|speaker:6|>",
+    "Liam (US)": "<|speaker:7|>",
+    "Emma (UK)": "<|speaker:8|>",
+    "Isabella (UK)": "<|speaker:9|>",
+    "Fable (UK)": "<|speaker:10|>",
+}
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Initialize models
+print("Downloading and initializing models...")
+
+
+def get_mimi_path():
+    """Get Mimi tokenizer weights from Hugging Face."""
+    repo_id = "kyutai/moshiko-mlx-bf16"
+    filename = "tokenizer-e351c8d8-checkpoint125.safetensors"
+    return hf_hub_download(repo_id, filename)
+
+
+dir = snapshot_download("jkeisling/smoltts_v0")
+mimi_path = get_mimi_path()
+# lm = LM(dir, dtype="bf16", device="cuda", version="dual_ar")
+codec = Tokenizer(mimi_path)
+
+
+# Naively split text into sentences
+def split_sentences(text):
+    sentences = re.split(r"(?<=[?.!])\s+", text)
+    return [s.strip() for s in sentences if s.strip()]
+
+
+@spaces.GPU
+def synthesize_speech(text, temperature, top_p, voice):
+    """Generate speech from text using Fish Speech, processing each sentence separately."""
+    lm = LM(dir, dtype="bf16", device="cuda", version="dual_ar")
+    sysprompt = voice_mapping.get(voice, "<|speaker:0|>")
+    sentences = split_sentences(text)
+    pcm_list = []
+
+    for sentence in sentences:
+        # Generate audio for each sentence individually
+        generated = lm([sentence], temp=temperature, top_p=top_p, sysprompt=sysprompt)
+        pcm = codec.decode(generated)
+        pcm_list.append(pcm.flatten())
+
+    # Concatenate all PCM arrays into one
+    final_pcm = np.concatenate(pcm_list)
+    return (24_000, final_pcm)
+
+
+# Create the Gradio interface
+with gr.Blocks(
+    theme=gr.themes.Default(
+        font=[gr.themes.GoogleFont("IBM Plex Sans"), "Arial", "sans-serif"],
+        font_mono=gr.themes.GoogleFont("IBM Plex Mono"),
+        primary_hue=gr.themes.colors.blue,
+        secondary_hue=gr.themes.colors.slate,
+    )
+) as demo:
+    with gr.Row():
+        gr.Markdown("""
+            # SmolTTS v0
+
+            SmolTTS v0 is an autoregressive 150M parameter character-level text-to-speech model pretrained with an [RQTransformer backbone](https://arxiv.org/abs/2203.01941) and paired with a pretrained [Mimi codec](https://arxiv.org/abs/2410.00037) vocoder. Designed for US and UK English, it was trained entirely on synthetic speech data generated using [Kokoro TTS](https://huggingface.co/hexgrad/Kokoro-82M). SmolTTS is Apache 2.0 licensed - enjoy!
+            """)
+
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                label="Input Text", placeholder="Enter text to synthesize...", lines=3
+            )
+            voice_dropdown = gr.Dropdown(
+                label="Voice",
+                choices=list(voice_mapping.keys()),
+                value="heart (US)",
+                info="Select a voice (sysprompt mapping)",
+            )
+            with gr.Row():
+                temperature = gr.Slider(
+                    minimum=0.0, maximum=1.0, value=0.1, step=0.1, label="Temperature"
+                )
+                top_p = gr.Slider(
+                    minimum=0.0, maximum=1.0, value=0.85, step=0.01, label="Top P"
+                )
+        with gr.Column():
+            audio_output = gr.Audio(label="Generated Speech", type="numpy")
+
+    generate_btn = gr.Button("Generate Speech", variant="primary")
+    generate_btn.click(
+        fn=synthesize_speech,
+        inputs=[input_text, temperature, top_p, voice_dropdown],
+        outputs=[audio_output],
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", share=False)

requirements.txt

@@ -0,0 +1,3 @@
+fish_speech_rs>=0.3.0
+rustymimi
+numpy