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LICENSE

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+MIT License
+
+Copyright (c) 2025
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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----
-title: AIapps
-emoji: 📈
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 6.1.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# മലയാളം Text → AI Voice (Free)
+
+A free web app (Hugging Face Space, Gradio) that converts **Malayalam** text to speech using the **AI4Bharat VITS** model.
+
+## How it works
+- Loads the multi‑lingual Indian **VITS TTS** model `ai4bharat/vits_rasa_13`, which includes **Malayalam** voices and multiple **styles** (NEWS, BOOK, etc.).
+- Renders a simple Gradio UI: paste Malayalam text → click **Generate** → download audio.
+
+> Model reference: AI4Bharat VITS model with Malayalam support and style/speaker IDs.  
+> Piper/Sherpa‑ONNX alternative for Malayalam also exists (`ml_IN-arjun`), if you prefer an ONNX path.  
+
+## Deploy (Hugging Face Spaces)
+1. Create a new Space → **Gradio**.  
+2. Upload these files: `app.py`, `requirements.txt`, `README.md`.  
+3. The Space will build and start automatically.  
+4. Share the public URL.
+
+## Usage
+- Default speaker is **MAL_F (11)**.  
+- Try styles like **NEWS (10)** for crisp reading, **BOOK (3)** for long‑form, **ALEXA (0)** for neutral.
+
+## Local run (optional)
+```bash
+python -m venv .venv && source .venv/bin/activate
+pip install -r requirements.txt
+python app.py
+```
+
+## Licensing
+- App code: MIT (see below).  
+- **Model license**: please review the license on the model page before commercial use.  
+
+### MIT License (app code)
+Copyright (c) 2025
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction...
+```
+(standard MIT terms)
+```
+
+
+## New features
+- **Prosody sliders:** speaking rate (0.5–1.5) & pitch (−4…+4 semitones). Implemented via resampling (approximate).
+- **Batch paragraphs:** split on blank lines → one file per paragraph × style.
+- **MP3 alongside WAV:** via `pydub` + ffmpeg (present on Spaces). Falls back to WAV if MP3 fails.
+

app.py

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+# app.py
+# Malayalam TTS (Free) – Multi-style, Prosody (rate & pitch), Batch paragraphs, WAV+MP3
+# Model: AI4Bharat VITS (supports Malayalam among 13 Indian languages)
+
+import gradio as gr
+import soundfile as sf
+import tempfile
+import torch
+from transformers import AutoModel, AutoTokenizer
+import numpy as np
+import os
+
+# Optional MP3 conversion
+try:
+    from pydub import AudioSegment
+    _HAS_PYDUB = True
+except Exception:
+    _HAS_PYDUB = False
+
+MODEL_ID = "ai4bharat/vits_rasa_13"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = AutoModel.from_pretrained(MODEL_ID, trust_remote_code=True).to(device)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+
+DEFAULT_SPEAKER = 11  # MAL_F
+DEFAULT_TEXT = (
+    "മലയാളം ടെക്സ്റ്റ് ശബ്ദമായി മാറ്റാൻ ഇതുപയോഗിക്കുക. താഴെ ഒരു ഉദാഹരണം നൽകുന്നു.\n\n"
+    "ഇത് ഒരു രണ്ടാം പാരագրാഫ് ആണ്."
+)
+
+STYLE_LABELS = {
+    0: "ALEXA",
+    1: "ANGER",
+    2: "BB",
+    3: "BOOK",
+    4: "CONV",
+    5: "DIGI",
+    6: "DISGUST",
+    7: "FEAR",
+    8: "HAPPY",
+    10: "NEWS",
+    12: "SAD",
+    14: "SURPRISE",
+    15: "UMANG",
+    16: "WIKI",
+}
+
+
+def split_paragraphs(text: str):
+    # Split on blank lines; ignore empty chunks
+    parts = [p.strip() for p in text.replace('\r','').split('\n\n')]
+    parts = [p for p in parts if p]
+    return parts if parts else ([text.strip()] if text.strip() else [])
+
+
+def time_scale(wav: np.ndarray, rate: float) -> np.ndarray:
+    """Naive time scaling by linear interpolation. rate>1 -> faster (shorter)."""
+    if rate <= 0:
+        rate = 1.0
+    if abs(rate - 1.0) < 1e-6:
+        return wav
+    n = len(wav)
+    new_len = max(1, int(n / rate))
+    x_old = np.linspace(0.0, 1.0, n, endpoint=False)
+    x_new = np.linspace(0.0, 1.0, new_len, endpoint=False)
+    return np.interp(x_new, x_old, wav).astype(wav.dtype)
+
+
+def apply_prosody(wav: np.ndarray, sr: int, rate: float, pitch_semitones: float):
+    """
+    Approximate prosody control without heavy DSP:
+    - We implement pitch by changing the output *sample rate* by factor pf = 2**(semitones/12).
+    - Changing sample rate also changes playback speed by pf, so we pre-scale time by rate/pf
+      to keep the final perceived speaking rate close to the requested rate.
+    """
+    pf = 2.0 ** (pitch_semitones / 12.0)
+    pre_rate = max(0.25, min(4.0, rate / max(pf, 1e-6)))
+    y = time_scale(wav, pre_rate)
+    out_sr = int(sr * pf)
+    return y, out_sr
+
+
+def synthesize_once(text: str, speaker_id: int, style_id: int):
+    inputs = tokenizer(text=text, return_tensors="pt").to(device)
+    outputs = model(inputs['input_ids'], speaker_id=int(speaker_id), emotion_id=int(style_id))
+    wav = outputs.waveform.squeeze().detach().cpu().numpy()
+    sr = model.config.sampling_rate
+    return wav, sr
+
+
+def save_audio_pair(wav: np.ndarray, sr: int, base_name: str, make_mp3: bool):
+    # Save WAV
+    wav_path = base_name + ".wav"
+    sf.write(wav_path, wav, sr)
+    out_files = [wav_path]
+    # Optionally save MP3 via pydub/ffmpeg
+    if make_mp3 and _HAS_PYDUB:
+        try:
+            mp3_path = base_name + ".mp3"
+            seg = AudioSegment.from_wav(wav_path)
+            seg.export(mp3_path, format="mp3")
+            out_files.append(mp3_path)
+        except Exception:
+            pass
+    return out_files
+
+
+def parse_style(choice: str) -> int:
+    try:
+        return int(choice.split(":", 1)[0])
+    except Exception:
+        return 0
+
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # മലയാളം Text → AI Voice (Free)
+        Open‑source Malayalam TTS powered by **AI4Bharat VITS**.
+        Now supports **multiple voice styles**, **prosody (rate & pitch)**, **batch paragraphs**, and **WAV + MP3** output.
+        """
+    )
+
+    with gr.Row():
+        txt = gr.Textbox(label="Malayalam Text (single or multiple paragraphs)", value=DEFAULT_TEXT, lines=8, placeholder="ഒരു അല്ലെങ്കിൽ നിരവധി പാരഗ്രാഫുകൾ ഇവിടെ പേസ്റ്റ് ചെയ്യുക… രണ്ട് newline ഉപയോഗിച്ച് വേർതിരിക്കുക.")
+    with gr.Row():
+        speaker = gr.Slider(0, 19, value=DEFAULT_SPEAKER, step=1, label="Speaker ID (MAL_F = 11)")
+        styles = gr.CheckboxGroup(
+            choices=[f"{k}:{v}" for k, v in STYLE_LABELS.items()],
+            value=["0:ALEXA", "10:NEWS", "3:BOOK"],
+            label="Voice styles (select one or more)"
+        )
+    with gr.Row():
+        rate = gr.Slider(minimum=0.5, maximum=1.5, value=1.0, step=0.05, label="Speaking rate (0.5–1.5)")
+        pitch = gr.Slider(minimum=-4, maximum=+4, value=0, step=1, label="Pitch (semitones, -4 to +4)")
+        batch = gr.Checkbox(value=True, label="Batch: split by blank lines (paragraphs)")
+        make_mp3 = gr.Checkbox(value=True, label="Also export MP3 (needs ffmpeg)")
+    with gr.Row():
+        btn = gr.Button("Generate", variant="primary")
+    audio = gr.Audio(label="Preview (first file)", type="filepath")
+    files_out = gr.Files(label="All generated files")
+    note = gr.Markdown()
+
+    def run(text, speaker_id, style_choices, rate, pitch, batch, make_mp3):
+        text = (text or "").strip()
+        if not text:
+            raise gr.Error("ദയവായി മലയാളത്തിൽ ഒരു വാചകം/പാരഗ്രാഫ് നൽകുക.")
+        paras = split_paragraphs(text) if batch else [text]
+        if not style_choices:
+            style_choices = ["0:ALEXA"]
+        total = len(paras) * len(style_choices)
+        if total > 30:
+            raise gr.Error(f"താങ്കൾ വളരെ കൂടുതൽ ഔട്ട്‌പുട്ടുകൾ ആവശ്യപ്പെടുന്നു ({total}). ദയവായി കുറച്ച് പാരഗ്രാഫുകൾ/സ്റ്റൈലുകൾ തിരഞ്ഞെടുക്കുക (<= 30 files).")
+
+        all_files = []
+        preview = None
+        details = []
+        idx = 1
+        for pi, para in enumerate(paras, start=1):
+            wav_raw, sr_raw = synthesize_once(para, int(speaker_id), parse_style(style_choices[0]))  # synthesize once per paragraph using first style to get base prosody; style will be applied per file below anyway
+            for sc in style_choices:
+                stid = parse_style(sc)
+                # Re-synthesize for each style to reflect emotion_id
+                wav, sr = synthesize_once(para, int(speaker_id), stid)
+                # Apply prosody approximation
+                wav2, sr2 = apply_prosody(wav, sr, float(rate), float(pitch))
+                base = tempfile.NamedTemporaryFile(suffix=".wav", delete=False).name[:-4]
+                base_named = f"{base}_p{pi:02d}_style-{stid}_{STYLE_LABELS.get(stid, 'STYLE')}"
+                outs = save_audio_pair(wav2, sr2, base_named, bool(make_mp3))
+                all_files.extend(outs)
+                if preview is None:
+                    preview = outs[0]
+                details.append(f"• P{pi} – {STYLE_LABELS.get(stid, sc)} → {os.path.basename(outs[0])}{' (+MP3)' if len(outs)>1 else ''}")
+                idx += 1
+
+        summary = (
+            f"Generated **{len(all_files)}** files for {len(paras)} paragraph(s) × {len(style_choices)} style(s).\n\n"
+            + "\n".join(details)
+            + ("\n\n**Note:** Rate & pitch are approximations using resampling; for studio-grade SSML prosody use a managed TTS like Azure." if True else "")
+        )
+        return preview, all_files, summary
+
+    btn.click(run, inputs=[txt, speaker, styles, rate, pitch, batch, make_mp3], outputs=[audio, files_out, note])
+
+    gr.Markdown(
+        """
+        **Prosody controls**  
+        *Speaking rate* slows/speeds audio; *Pitch* raises/lowers tone (in semitones). These are **approximate** controls based on resampling. For high‑fidelity prosody, consider SSML in Azure TTS.
+        
+        **Batch mode**  
+        Split input into paragraphs using a blank line. The app creates one file per **paragraph × style**.
+        
+        **MP3 output**  
+        Requires `ffmpeg` (available on Hugging Face Spaces). If unavailable, only WAV will be produced.
+        """
+    )
+
+if __name__ == "__main__":
+    demo.launch()

packages.txt

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+ffmpeg

requirements.txt

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+gradio==4.44.0
+transformers
+torch
+soundfile
+
+pydub