fix: stt.yaml env var WHISPER_URL→STT_URL + tts.py improvements

- stt.yaml: rename WHISPER_URL to STT_URL to match what stt.py reads
- tts.py: improve WAV handling (BytesIO fix), sentence splitting, robust
  _read_wav_bytes with wave+soundfile+raw-PCM fallbacks
- Add __pycache__/ to .gitignore

.gitignore

@@ -0,0 +1 @@
+__pycache__/

stt.yaml

@@ -28,7 +28,7 @@ spec:
               name: http
               protocol: TCP
           env:
-            - name: WHISPER_URL
+            - name: STT_URL
               # Ray Serve endpoint - routes to /whisper prefix
               value: "http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/whisper"
             - name: MLFLOW_TRACKING_URI

tts.py

@@ -5,19 +5,20 @@ TTS Demo - Gradio UI for testing Text-to-Speech service.
 Features:
 - Text input with language selection
 - Audio playback of synthesized speech
-- Voice/speaker selection (when available)
+- Sentence-level chunking for better quality
+- Speed control
 - MLflow metrics logging
-- Multiple TTS backends support (Coqui XTTS, Piper, etc.)
 """
 
 import os
+import re
 import time
 import logging
 import io
+import wave
 
 import gradio as gr
 import httpx
-import soundfile as sf
 import numpy as np
 
 from theme import get_lab_theme, CUSTOM_CSS, create_footer
@@ -126,42 +127,228 @@ LANGUAGES = {
     "Hungarian": "hu",
 }
 
+# ─── Text preprocessing ─────────────────────────────────────────────────
+
+_SENTENCE_RE = re.compile(r"(?<=[.!?;])\s+|(?<=\n)\s*", re.MULTILINE)
+
+_DIGIT_WORDS = {
+    "0": "zero", "1": "one", "2": "two", "3": "three", "4": "four",
+    "5": "five", "6": "six", "7": "seven", "8": "eight", "9": "nine",
+}
+
+
+def _expand_numbers(text: str) -> str:
+    """Expand standalone single digits to words for clearer pronunciation."""
+    return re.sub(
+        r"\b(\d)\b",
+        lambda m: _DIGIT_WORDS.get(m.group(0), m.group(0)),
+        text,
+    )
+
+
+def _clean_text(text: str) -> str:
+    """Clean and normalise text for TTS input."""
+    text = re.sub(r"[ \t]+", " ", text)
+    text = "\n".join(line.strip() for line in text.splitlines())
+    # Strip markdown / code-fence characters
+    text = re.sub(r"[*#~`|<>{}[\]\\]", "", text)
+    # Expand common symbols
+    text = text.replace("&", " and ")
+    text = text.replace("@", " at ")
+    text = text.replace("%", " percent ")
+    text = text.replace("+", " plus ")
+    text = text.replace("=", " equals ")
+    text = _expand_numbers(text)
+    return text.strip()
+
+
+def _split_sentences(text: str) -> list[str]:
+    """Split text into sentences suitable for TTS.
+
+    Keeps sentences short for best quality while preserving natural phrasing.
+    Very long segments are further split on commas / semicolons.
+    """
+    text = _clean_text(text)
+    if not text:
+        return []
+
+    raw_parts = _SENTENCE_RE.split(text)
+    sentences: list[str] = []
+    for part in raw_parts:
+        part = part.strip()
+        if not part:
+            continue
+        if len(part) > 200:
+            for sp in re.split(r"(?<=[,;])\s+", part):
+                sp = sp.strip()
+                if sp:
+                    sentences.append(sp)
+        else:
+            sentences.append(part)
+    return sentences
+
+
+# ─── Audio helpers ───────────────────────────────────────────────────────
+
+def _read_wav_bytes(data: bytes) -> tuple[int, np.ndarray]:
+    """Read WAV audio from bytes, handling scipy wavfile and standard WAV.
+
+    Returns (sample_rate, float32_audio) with values in [-1, 1].
+    """
+    buf = io.BytesIO(data)
+
+    # Try stdlib wave module first — most robust for PCM WAV from scipy
+    try:
+        with wave.open(buf, "rb") as wf:
+            sr = wf.getframerate()
+            n_frames = wf.getnframes()
+            n_channels = wf.getnchannels()
+            sampwidth = wf.getsampwidth()
+            raw = wf.readframes(n_frames)
+
+        if sampwidth == 2:
+            audio = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768.0
+        elif sampwidth == 4:
+            audio = np.frombuffer(raw, dtype=np.int32).astype(np.float32) / 2147483648.0
+        elif sampwidth == 1:
+            audio = (np.frombuffer(raw, dtype=np.uint8).astype(np.float32) - 128.0) / 128.0
+        else:
+            raise ValueError(f"Unsupported sample width: {sampwidth}")
+
+        if n_channels > 1:
+            audio = audio.reshape(-1, n_channels).mean(axis=1)
+
+        return sr, audio
+    except Exception as exc:
+        logger.debug("wave module failed (%s), trying soundfile", exc)
+
+    # Fallback: soundfile (handles FLAC, OGG, etc.)
+    buf.seek(0)
+    try:
+        import soundfile as sf
+
+        audio, sr = sf.read(buf, dtype="float32")
+        if audio.ndim > 1:
+            audio = audio.mean(axis=1)
+        return sr, audio
+    except Exception as exc:
+        logger.debug("soundfile failed (%s), attempting raw PCM", exc)
+
+    # Last resort: raw 16-bit PCM at 22050 Hz
+    logger.warning(
+        "Could not parse WAV header (len=%d, first 4 bytes=%r); raw PCM decode",
+        len(data), data[:4],
+    )
+    audio = np.frombuffer(data, dtype=np.int16).astype(np.float32) / 32768.0
+    return 22050, audio
+
+
+def _concat_audio(
+    chunks: list[tuple[int, np.ndarray]], pause_ms: int = 200
+) -> tuple[int, np.ndarray]:
+    """Concatenate (sample_rate, audio) chunks with silence gaps."""
+    if not chunks:
+        return 22050, np.array([], dtype=np.float32)
+    if len(chunks) == 1:
+        return chunks[0]
+
+    sr = chunks[0][0]
+    silence = np.zeros(int(sr * pause_ms / 1000), dtype=np.float32)
+
+    parts: list[np.ndarray] = []
+    for sample_rate, audio in chunks:
+        if sample_rate != sr:
+            ratio = sr / sample_rate
+            indices = np.arange(0, len(audio), 1.0 / ratio).astype(int)
+            indices = indices[indices < len(audio)]
+            audio = audio[indices]
+        parts.append(audio)
+        parts.append(silence)
+
+    if parts:
+        parts.pop()  # remove trailing silence
+    return sr, np.concatenate(parts)
+
+
+# ─── TTS synthesis ───────────────────────────────────────────────────────
+
+def _synthesize_chunk(text: str, lang_code: str, speed: float = 1.0) -> bytes:
+    """Synthesize a single text chunk via the TTS backend.
+
+    Uses the JSON POST endpoint (no URL length limits, supports speed).
+    Falls back to the Coqui-compatible GET endpoint if POST fails.
+    """
+    import base64 as b64
+
+    # Try JSON POST first
+    try:
+        resp = client.post(
+            TTS_URL,
+            json={
+                "text": text,
+                "language": lang_code,
+                "speed": speed,
+                "return_base64": True,
+            },
+        )
+        resp.raise_for_status()
+        ct = resp.headers.get("content-type", "")
+        if "application/json" in ct:
+            body = resp.json()
+            if "error" in body:
+                raise RuntimeError(body["error"])
+            audio_b64 = body.get("audio", "")
+            if audio_b64:
+                return b64.b64decode(audio_b64)
+        # Non-JSON response — treat as raw audio bytes
+        return resp.content
+    except Exception:
+        logger.debug("POST endpoint failed, falling back to GET /api/tts", exc_info=True)
+
+    # Fallback: Coqui-compatible GET (no speed control)
+    resp = client.get(
+        f"{TTS_URL}/api/tts",
+        params={"text": text, "language_id": lang_code},
+    )
+    resp.raise_for_status()
+    return resp.content
+
 
 def synthesize_speech(
-    text: str, language: str
+    text: str, language: str, speed: float
 ) -> tuple[str, tuple[int, np.ndarray] | None, str]:
-    """Synthesize speech from text using the TTS service."""
+    """Synthesize speech from text using the TTS service.
+
+    Long text is split into sentences and synthesized individually
+    for better quality, then concatenated with natural pauses.
+    """
     if not text.strip():
         return "❌ Please enter some text", None, ""
 
     lang_code = LANGUAGES.get(language, "en")
+    sentences = _split_sentences(text)
+    if not sentences:
+        return "❌ No speakable text found after cleaning", None, ""
 
     try:
         start_time = time.time()
+        audio_chunks: list[tuple[int, np.ndarray]] = []
 
-        # Call TTS service (Coqui XTTS API format)
+        for sentence in sentences:
-        response = client.get(
+            raw_audio = _synthesize_chunk(sentence, lang_code, speed)
-            f"{TTS_URL}/api/tts", params={"text": text, "language_id": lang_code}
+            sr, audio = _read_wav_bytes(raw_audio)
-        )
+            audio_chunks.append((sr, audio))
-        response.raise_for_status()
 
+        sample_rate, audio_data = _concat_audio(audio_chunks)
         latency = time.time() - start_time
-        audio_bytes = response.content
+        duration = len(audio_data) / sample_rate if sample_rate > 0 else 0
 
-        # Parse audio data
+        n_chunks = len(sentences)
-        audio_io = io.BytesIO(audio_bytes)
+        status = (
-        audio_data, sample_rate = sf.read(audio_io)
+            f"✅ Generated {duration:.2f}s of audio in {latency * 1000:.0f}ms"
+            f" ({n_chunks} sentence{'s' if n_chunks != 1 else ''})"
+        )
 
-        # Calculate duration
-        if len(audio_data.shape) == 1:
-            duration = len(audio_data) / sample_rate
-        else:
-            duration = len(audio_data) / sample_rate
-
-        # Status message
-        status = f"✅ Generated {duration:.2f}s of audio in {latency * 1000:.0f}ms"
-
-        # Log to MLflow
         _log_tts_metrics(
             latency=latency,
             audio_duration=duration,
@@ -169,19 +356,19 @@ def synthesize_speech(
             language=lang_code,
         )
 
-        # Metrics
         metrics = f"""
 **Audio Statistics:**
 - Duration: {duration:.2f} seconds
 - Sample Rate: {sample_rate} Hz
-- Size: {len(audio_bytes) / 1024:.1f} KB
+- Size: {len(audio_data) * 2 / 1024:.1f} KB
 - Generation Time: {latency * 1000:.0f}ms
 - Real-time Factor: {latency / duration:.2f}x
 - Language: {language} ({lang_code})
+- Speed: {speed:.1f}x
+- Sentences: {n_chunks}
 - Characters: {len(text)}
 - Chars/sec: {len(text) / latency:.1f}
 """
-
         return status, (sample_rate, audio_data), metrics
 
     except httpx.HTTPStatusError as e:
@@ -189,37 +376,33 @@ def synthesize_speech(
         return f"❌ TTS service error: {e.response.status_code}", None, ""
     except Exception as e:
         logger.exception("TTS synthesis failed")
-        return f"❌ Error: {str(e)}", None, ""
+        return f"❌ Error: {e}", None, ""
 
 
 def check_service_health() -> str:
     """Check if the TTS service is healthy."""
     try:
-        # Try the health endpoint first
         response = client.get(f"{TTS_URL}/health", timeout=5.0)
         if response.status_code == 200:
             return "🟢 Service is healthy"
-
-        # Fall back to root endpoint
         response = client.get(f"{TTS_URL}/", timeout=5.0)
         if response.status_code == 200:
             return "🟢 Service is responding"
-
         return f"🟡 Service returned status {response.status_code}"
     except Exception as e:
-        return f"🔴 Service unavailable: {str(e)}"
+        return f"🔴 Service unavailable: {e}"
 
 
-# Build the Gradio app
+# ─── Gradio UI ───────────────────────────────────────────────────────────
+
 with gr.Blocks(theme=get_lab_theme(), css=CUSTOM_CSS, title="TTS Demo") as demo:
     gr.Markdown("""
 # 🔊 Text-to-Speech Demo
 
 Test the **Coqui XTTS** text-to-speech service. Convert text to natural-sounding speech
-in multiple languages.
+in multiple languages. Long text is automatically split into sentences for better quality.
 """)
 
-    # Service status
     with gr.Row():
         health_btn = gr.Button("🔄 Check Service", size="sm")
         health_status = gr.Textbox(label="Service Status", interactive=False)
@@ -227,7 +410,6 @@ in multiple languages.
     health_btn.click(fn=check_service_health, outputs=health_status)
 
     with gr.Tabs():
-        # Tab 1: Basic TTS
         with gr.TabItem("🎤 Text to Speech"):
             with gr.Row():
                 with gr.Column(scale=2):
@@ -237,17 +419,19 @@ in multiple languages.
                         lines=5,
                         max_lines=10,
                     )
-
                     with gr.Row():
                         language = gr.Dropdown(
                             choices=list(LANGUAGES.keys()),
                             value="English",
                             label="Language",
                         )
-                        synthesize_btn = gr.Button(
+                        speed = gr.Slider(
-                            "🔊 Synthesize", variant="primary", scale=2
+                            minimum=0.5, maximum=2.0, value=1.0,
+                            step=0.1, label="Speed",
+                        )
+                        synthesize_btn = gr.Button(
+                            "🔊 Synthesize", variant="primary", scale=2,
                         )
-
                 with gr.Column(scale=1):
                     status_output = gr.Textbox(label="Status", interactive=False)
                     metrics_output = gr.Markdown(label="Metrics")
@@ -256,46 +440,31 @@ in multiple languages.
 
             synthesize_btn.click(
                 fn=synthesize_speech,
-                inputs=[text_input, language],
+                inputs=[text_input, language, speed],
                 outputs=[status_output, audio_output, metrics_output],
             )
 
-            # Example texts
             gr.Examples(
                 examples=[
-                    [
+                    ["Hello! Welcome to Davies Tech Labs. This is a demonstration of our text-to-speech system.", "English", 1.0],
-                        "Hello! Welcome to Davies Tech Labs. This is a demonstration of our text-to-speech system.",
+                    ["The quick brown fox jumps over the lazy dog. This sentence contains every letter of the alphabet.", "English", 1.0],
-                        "English",
+                    ["Bonjour! Bienvenue au laboratoire technique de Davies.", "French", 1.0],
-                    ],
+                    ["Hola! Bienvenido al laboratorio de tecnología.", "Spanish", 1.0],
-                    [
+                    ["Guten Tag! Willkommen im Techniklabor.", "German", 1.0],
-                        "The quick brown fox jumps over the lazy dog. This sentence contains every letter of the alphabet.",
-                        "English",
-                    ],
-                    [
-                        "Bonjour! Bienvenue au laboratoire technique de Davies.",
-                        "French",
-                    ],
-                    ["Hola! Bienvenido al laboratorio de tecnología.", "Spanish"],
-                    ["Guten Tag! Willkommen im Techniklabor.", "German"],
                 ],
-                inputs=[text_input, language],
+                inputs=[text_input, language, speed],
             )
 
-        # Tab 2: Comparison
         with gr.TabItem("🔄 Language Comparison"):
             gr.Markdown("Compare the same text in different languages.")
 
             compare_text = gr.Textbox(
                 label="Text to Compare", value="Hello, how are you today?", lines=2
             )
-
             with gr.Row():
-                lang1 = gr.Dropdown(
+                lang1 = gr.Dropdown(choices=list(LANGUAGES.keys()), value="English", label="Language 1")
-                    choices=list(LANGUAGES.keys()), value="English", label="Language 1"
+                lang2 = gr.Dropdown(choices=list(LANGUAGES.keys()), value="Spanish", label="Language 2")
-                )
+                compare_speed = gr.Slider(minimum=0.5, maximum=2.0, value=1.0, step=0.1, label="Speed")
-                lang2 = gr.Dropdown(
-                    choices=list(LANGUAGES.keys()), value="Spanish", label="Language 2"
-                )
 
             compare_btn = gr.Button("Compare Languages", variant="primary")
 
@@ -304,24 +473,22 @@ in multiple languages.
                     gr.Markdown("### Language 1")
                     audio1 = gr.Audio(label="Audio 1", type="numpy")
                     status1 = gr.Textbox(label="Status", interactive=False)
-
                 with gr.Column():
                     gr.Markdown("### Language 2")
                     audio2 = gr.Audio(label="Audio 2", type="numpy")
                     status2 = gr.Textbox(label="Status", interactive=False)
 
-            def compare_languages(text, l1, l2):
+            def compare_languages(text, l1, l2, spd):
-                s1, a1, _ = synthesize_speech(text, l1)
+                s1, a1, _ = synthesize_speech(text, l1, spd)
-                s2, a2, _ = synthesize_speech(text, l2)
+                s2, a2, _ = synthesize_speech(text, l2, spd)
                 return s1, a1, s2, a2
 
             compare_btn.click(
                 fn=compare_languages,
-                inputs=[compare_text, lang1, lang2],
+                inputs=[compare_text, lang1, lang2, compare_speed],
                 outputs=[status1, audio1, status2, audio2],
             )
 
-        # Tab 3: Batch Processing
         with gr.TabItem("📚 Batch Synthesis"):
             gr.Markdown("Synthesize multiple texts at once (one per line).")
 
@@ -330,22 +497,26 @@ in multiple languages.
                 placeholder="Enter multiple texts, one per line...",
                 lines=6,
             )
-            batch_lang = gr.Dropdown(
+            batch_lang = gr.Dropdown(choices=list(LANGUAGES.keys()), value="English", label="Language")
-                choices=list(LANGUAGES.keys()), value="English", label="Language"
+            batch_speed = gr.Slider(minimum=0.5, maximum=2.0, value=1.0, step=0.1, label="Speed")
-            )
             batch_btn = gr.Button("Synthesize All", variant="primary")
 
             batch_status = gr.Textbox(label="Status", interactive=False)
-            batch_audios = gr.Dataset(
+            batch_audio = gr.Audio(label="Combined Audio", type="numpy")
-                components=[gr.Audio(type="numpy")], label="Generated Audio Files"
-            )
 
-            # Note: Batch processing would need more complex handling
+            def batch_synthesize(texts_raw: str, lang: str, spd: float):
-            # This is a simplified version
+                lines = [l.strip() for l in texts_raw.strip().splitlines() if l.strip()]
-            gr.Markdown("""
+                if not lines:
-*Note: For batch processing of many texts, consider using the API directly
+                    return "❌ Please enter at least one line of text", None
-or the Kubeflow pipeline for better throughput.*
+                combined = "\n".join(lines)
-""")
+                status, audio, _ = synthesize_speech(combined, lang, spd)
+                return status, audio
+
+            batch_btn.click(
+                fn=batch_synthesize,
+                inputs=[batch_input, batch_lang, batch_speed],
+                outputs=[batch_status, batch_audio],
+            )
 
     create_footer()