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README (2).md

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+---
+title: Rule-Integrated Chichewa TTS
+emoji: 👩‍🎤
+colorFrom: yellow
+colorTo: blue
+sdk: gradio
+sdk_version: 6.9.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app (1).py

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+import gradio as gr
+import numpy as np
+import torch
+import tempfile
+import os
+from scipy.io.wavfile import write
+from transformers import (
+    SpeechT5Processor,
+    SpeechT5ForTextToSpeech,
+    SpeechT5HifiGan
+)
+
+# =========================
+# Model loading
+# =========================
+checkpoint = "Chithekitale/Chichewa_tts_v2"
+
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+# Make all keys consistent
+speaker_embeddings = {
+    "SPK1": "spkemb/speaker_2.npy",
+    "SPK2": "spkemb/speaker_1.npy",
+    "SPK3": "spkemb/cmu_us_ksp_arctic-wav-arctic_b0087.npy",
+    "SPK4": "spkemb/cmu_us_rms_arctic-wav-arctic_b0353.npy",
+    "SPK5": "spkemb/cmu_us_slt_arctic-wav-arctic_a0508.npy",
+}
+
+SPEAKER_CHOICES = [
+    "SPK1 (female)",
+    "SPK2 (male)",
+    "SPK3 (male)",
+    "SPK4 (male)",
+    "SPK5 (female)"
+]
+
+EXAMPLES = [
+    ["Ndapita, koma ndibweranso pompano.", "SPK1 (female)"],
+    ["Koma apapa zikuoneka kuti ziyenda bwino.", "SPK2 (male)"],
+    ["Ineyo ndikuona kuti sizizasithanso.", "SPK3 (male)"],
+    ["Mwina kusogolo kuno anthu ena azalimba mtima, koma panopana ndakaika.", "SPK4 (male)"],
+    ["Simungasankhe munthu oti bola linamukana.", "SPK5 (female)"],
+    ["Kodi chimanga panopa chikugulisidwa zingati, kapena nanunso simukudziwa?", "SPK5 (female)"],
+]
+
+SAMPLE_RATE = 16000
+
+# =========================
+# Helpers
+# =========================
+def get_speaker_key(speaker_label: str) -> str:
+    # "SPK1 (female)" -> "SPK1"
+    return speaker_label.split()[0]
+
+def load_speaker_embedding(speaker: str) -> np.ndarray:
+    speaker_key = get_speaker_key(speaker)
+
+    if speaker_key not in speaker_embeddings:
+        raise ValueError(f"Unknown speaker key: {speaker_key}")
+
+    path = speaker_embeddings[speaker_key]
+
+    try:
+        speaker_embedding = np.load(path).astype(np.float32)
+    except Exception as e:
+        raise FileNotFoundError(
+            f"Could not load speaker embedding file: {path}. Error: {e}"
+        )
+
+    if speaker_embedding.ndim == 2:
+        speaker_embedding = speaker_embedding.mean(axis=0)
+
+    speaker_embedding = np.squeeze(speaker_embedding)
+
+    if speaker_embedding.shape != (512,):
+        raise ValueError(
+            f"Unexpected speaker embedding shape after processing: "
+            f"{speaker_embedding.shape}. Expected (512,)"
+        )
+
+    return speaker_embedding
+
+def save_audio_to_wav(audio: np.ndarray, sample_rate: int = SAMPLE_RATE) -> str:
+    """
+    Save generated int16 audio to a temporary WAV file and return its path.
+    """
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
+    temp_file.close()
+    write(temp_file.name, sample_rate, audio)
+    return temp_file.name
+
+# =========================
+# Inference
+# =========================
+def predict(text, speaker):
+    try:
+        if not text or len(text.strip()) == 0:
+            return None, None, "Please enter some Chichewa text."
+
+        inputs = processor(text=text, return_tensors="pt")
+        input_ids = inputs["input_ids"][..., :model.config.max_text_positions]
+
+        speaker_embedding = load_speaker_embedding(speaker)
+        speaker_embedding = torch.tensor(
+            speaker_embedding, dtype=torch.float32
+        ).unsqueeze(0)
+
+        with torch.no_grad():
+            speech = model.generate_speech(
+                input_ids,
+                speaker_embedding,
+                vocoder=vocoder
+            )
+
+        speech = speech.cpu().numpy()
+
+        # Normalize safely before int16 conversion
+        max_val = np.max(np.abs(speech))
+        if max_val > 0:
+            speech = speech / max_val
+
+        speech = (speech * 32767).astype(np.int16)
+
+        # Save WAV file for downloading
+        wav_path = save_audio_to_wav(speech, SAMPLE_RATE)
+
+        status = f"Generated speech successfully using speaker: {speaker}"
+        return (SAMPLE_RATE, speech), wav_path, status
+
+    except Exception as e:
+        return None, None, f"Error during generation: {str(e)}"
+
+def clear_all():
+    return "", "SPK1 (female)", None, None, "Ready."
+
+# =========================
+# UI
+# =========================
+custom_css = """
+.gradio-container {
+    max-width: 1100px !important;
+    margin: 0 auto;
+}
+.hero {
+    text-align: center;
+    padding: 10px 0 0 0;
+}
+.section-note {
+    font-size: 0.95rem;
+    opacity: 0.9;
+}
+"""
+
+with gr.Blocks(css=custom_css, title="Chichewa Speech Synthesis Demo") as demo:
+    gr.HTML(
+        """
+        <div class="hero">
+            <h1>Rule-Intergrated Chichewa Speech Synthesis</h1>
+            <p class="section-note">
+                Enter Chichewa text, choose a speaker voice, and generate speech audio.
+            </p>
+        </div>
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=5):
+            text_input = gr.Textbox(
+                label="Input Text",
+                placeholder="Type Chichewa text here...",
+                lines=6
+            )
+
+            speaker_input = gr.Radio(
+                label="Speaker Voice",
+                choices=SPEAKER_CHOICES,
+                value="SPK1 (female)"
+            )
+
+            with gr.Row():
+                generate_btn = gr.Button("Generate Speech", variant="primary")
+                clear_btn = gr.Button("Clear")
+
+            status_box = gr.Textbox(
+                label="System Status",
+                value="Ready.",
+                interactive=False
+            )
+
+        with gr.Column(scale=5):
+            audio_output = gr.Audio(
+                label="Generated Speech",
+                type="numpy",
+                autoplay=False
+            )
+
+            download_file = gr.File(
+                label="Download Audio File"
+            )
+
+    gr.Markdown("### Example Inputs")
+    gr.Examples(
+        examples=EXAMPLES,
+        inputs=[text_input, speaker_input]
+    )
+
+    generate_btn.click(
+        fn=predict,
+        inputs=[text_input, speaker_input],
+        outputs=[audio_output, download_file, status_box],
+        show_progress="full"
+    )
+
+    clear_btn.click(
+        fn=clear_all,
+        inputs=[],
+        outputs=[text_input, speaker_input, audio_output, download_file, status_box]
+    )
+
+demo.launch()

gitignore (1)

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+*.pyc
+__pycache__/
+.DS_Store
+

requirements (1).txt

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+git+https://github.com/huggingface/transformers.git
+torch
+torchaudio
+soundfile
+librosa
+samplerate
+resampy
+sentencepiece