Initial Gradio demo

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+reference.wav filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1,15 @@
 ---
-title: YorubaEnglish CodeSwitching TTS Demo
-emoji: 🚀
-colorFrom: pink
-colorTo: blue
+title: Yoruba English Code Switching TTS
+emoji: 🎙️
+colorFrom: green
+colorTo: yellow
 sdk: gradio
-sdk_version: 6.20.0
-python_version: '3.13'
+sdk_version: 5.6.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Interactive demo for [LyngualLabs/YorubaEnglish-CodeSwitching-TTS](https://huggingface.co/LyngualLabs/YorubaEnglish-CodeSwitching-TTS) —
+a voice-cloning Yoruba/Yoruba-English TTS model, full fine-tuned from VoxCPM2 on
+~1,039 hours of pooled Yoruba speech data.

app.py

@@ -0,0 +1,91 @@
+import os
+import gradio as gr
+import soundfile as sf
+from huggingface_hub import snapshot_download
+
+MODEL_REPO = "LyngualLabs/YorubaEnglish-CodeSwitching-TTS"
+DEFAULT_REF_WAV = "reference.wav"
+DEFAULT_REF_TEXT = "Ìròyìn ti sọ pé the government will ensure electricity tariff goes down ní January."
+
+print("Downloading model...", flush=True)
+model_dir = snapshot_download(MODEL_REPO)
+
+from voxcpm import VoxCPM  # noqa: E402  (import after snapshot_download so the repo is cached)
+
+print("Loading model...", flush=True)
+model = VoxCPM.from_pretrained(model_dir, load_denoiser=False)
+SR = model.tts_model.sample_rate
+print("Model loaded.", flush=True)
+
+
+def synthesize(text, ref_audio, ref_text, cfg_value, inference_timesteps):
+    if not text or not text.strip():
+        raise gr.Error("Please enter some text to synthesize.")
+
+    prompt_wav_path = ref_audio if ref_audio else DEFAULT_REF_WAV
+    prompt_text = ref_text.strip() if (ref_audio and ref_text and ref_text.strip()) else (
+        DEFAULT_REF_TEXT if not ref_audio else ""
+    )
+    if ref_audio and not prompt_text:
+        raise gr.Error("Please provide the exact transcript of your uploaded reference audio.")
+
+    wav = model.generate(
+        text=text.strip(),
+        prompt_wav_path=prompt_wav_path,
+        prompt_text=prompt_text,
+        cfg_value=cfg_value,
+        inference_timesteps=int(inference_timesteps),
+    )
+    out_path = "output.wav"
+    sf.write(out_path, wav, SR)
+    return out_path
+
+
+EXAMPLES = [
+    ["Mo ní meeting pẹ̀lú marketing team ní aago mẹ́wàá lónìí, nítorí náà ẹ jẹ́ ká yára."],
+    ["Ìròyìn ti sọ pé the government will ensure electricity tariff goes down ní January."],
+    ["Wọ́n ní ìpàdé ní hotel tó wà ní Victoria Island, kì í ṣe ní office wa."],
+]
+
+with gr.Blocks(title="Yoruba-English Code-Switching TTS") as demo:
+    gr.Markdown(
+        """
+        # 🎙️ Yoruba–English Code-Switching TTS
+        A voice-cloning text-to-speech model for Yoruba, including natural
+        Yoruba–English code-switching — full fine-tuned from
+        [VoxCPM2](https://huggingface.co/openbmb/VoxCPM2) on ~1,039 hours pooled from
+        DSN African Voices, NaijaVoices, YECS, and WAXAL.
+
+        Model card: [LyngualLabs/YorubaEnglish-CodeSwitching-TTS](https://huggingface.co/LyngualLabs/YorubaEnglish-CodeSwitching-TTS)
+
+        ⚠️ **Experimental** — trained for only ~1 epoch. Quality varies by sentence;
+        rarer words/proper nouns are more likely to be mispronounced.
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+            text_in = gr.Textbox(
+                label="Text to speak",
+                placeholder="Type Yoruba, English, or mixed Yoruba-English text...",
+                lines=4,
+            )
+            with gr.Accordion("Voice cloning (optional) — upload your own reference", open=False):
+                ref_audio = gr.Audio(label="Reference audio (3-10s, clean)", type="filepath")
+                ref_text = gr.Textbox(label="Exact transcript of the reference audio")
+                gr.Markdown("_Leave blank to use the default built-in voice._")
+            with gr.Accordion("Advanced", open=False):
+                cfg_value = gr.Slider(1.0, 4.0, value=2.0, step=0.1, label="cfg_value (higher = sticks closer to reference)")
+                inference_timesteps = gr.Slider(10, 35, value=22, step=1, label="inference_timesteps (higher = smoother, slower)")
+            btn = gr.Button("Generate", variant="primary")
+        with gr.Column():
+            audio_out = gr.Audio(label="Generated speech", type="filepath")
+
+    gr.Examples(examples=EXAMPLES, inputs=[text_in])
+    btn.click(
+        synthesize,
+        inputs=[text_in, ref_audio, ref_text, cfg_value, inference_timesteps],
+        outputs=[audio_out],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

reference.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ac05c7ce4b2afe926ffa2dcf8af0d194adcfed9a28862d7139069a28916fa56
+size 537644

requirements.txt

@@ -0,0 +1,4 @@
+git+https://github.com/OpenBMB/VoxCPM.git
+gradio
+soundfile
+huggingface_hub