inital commit

app.py

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+import gradio as gr
+import random
+import re
+import difflib
+import torch
+from functools import lru_cache
+from transformers import pipeline
+
+# -------- Sentences to practice (customize freely) ----------
+SENTENCE_BANK = [
+    "The quick brown fox jumps over the lazy dog.",
+    "I promise to speak clearly and at a steady pace.",
+    "Open source makes AI more transparent and inclusive.",
+    "Hugging Face Spaces make demos easy to share.",
+    "Today the weather in Berlin is pleasantly cool.",
+    "Privacy and transparency should go hand in hand.",
+    "Please generate a new sentence for me to read.",
+    "Machine learning can amplify or reduce inequality.",
+    "Responsible AI requires participation from everyone.",
+    "This microphone test checks my pronunciation accuracy.",
+]
+
+# -------- Utilities ----------
+def normalize_text(t: str) -> str:
+    t = t.lower()
+    # keep letters and numbers, replace anything else with space
+    t = re.sub(r"[^a-z0-9'äöüßçéèêáàóòúùîïôñ\-]+", " ", t)
+    # collapse whitespace
+    t = re.sub(r"\s+", " ", t).strip()
+    return t
+
+def similarity_and_diff(ref: str, hyp: str):
+    """Return similarity ratio (0..1) and HTML diff highlighting changes."""
+    ref_tokens = ref.split()
+    hyp_tokens = hyp.split()
+    sm = difflib.SequenceMatcher(a=ref_tokens, b=hyp_tokens)
+    ratio = sm.ratio()
+
+    # Build HTML with insertions/deletions highlighted
+    out = []
+    for op, i1, i2, j1, j2 in sm.get_opcodes():
+        if op == "equal":
+            out.append(" " + " ".join(ref_tokens[i1:i2]))
+        elif op == "delete":
+            out.append(' <span style="background:#ffe0e0;text-decoration:line-through;">'
+                       + " ".join(ref_tokens[i1:i2]) + "</span>")
+        elif op == "insert":
+            out.append(' <span style="background:#e0ffe0;">'
+                       + " ".join(hyp_tokens[j1:j2]) + "</span>")
+        elif op == "replace":
+            out.append(' <span style="background:#ffe0e0;text-decoration:line-through;">'
+                       + " ".join(ref_tokens[i1:i2]) + "</span>")
+            out.append(' <span style="background:#e0ffe0;">'
+                       + " ".join(hyp_tokens[j1:j2]) + "</span>")
+    html = '<div style="line-height:1.6;font-size:1rem;">' + "".join(out).strip() + "</div>"
+    return ratio, html
+
+@lru_cache(maxsize=2)
+def get_asr(model_id: str, device_preference: str):
+    """Cache an ASR pipeline. device_preference: 'auto'|'cpu'|'cuda'."""
+    if device_preference == "cuda" and torch.cuda.is_available():
+        device = 0
+    elif device_preference == "auto":
+        device = 0 if torch.cuda.is_available() else -1
+    else:
+        device = -1
+    return pipeline(
+        "automatic-speech-recognition",
+        model=model_id,
+        device=device,
+        chunk_length_s=30,
+        return_timestamps=False,
+    )
+
+def gen_sentence():
+    return random.choice(SENTENCE_BANK)
+
+def check_pronunciation(audio_path, target_sentence, model_id, lang, device_pref, pass_threshold):
+    if not target_sentence:
+        return gr.update(value=""), gr.update(value=""), gr.update(value=""), gr.update(value="Please generate a sentence first.")
+
+    asr = get_asr(model_id, device_pref)
+    # Whisper models accept a 'generate' kwarg with language hints via tokenizer, but
+    # transformers pipeline exposes it as 'generate_kwargs' for whisper models.
+    try:
+        result = asr(audio_path, generate_kwargs={"language": lang} if lang else None)
+        hyp_raw = result["text"].strip()
+    except Exception as e:
+        return "", "", "", f"Transcription failed: {e}"
+
+    ref_norm = normalize_text(target_sentence)
+    hyp_norm = normalize_text(hyp_raw)
+
+    ratio, diff_html = similarity_and_diff(ref_norm, hyp_norm)
+    passed = ratio >= pass_threshold
+
+    summary = (
+        f"✅ Correct (≥ {int(pass_threshold*100)}%)"
+        if passed else
+        f"❌ Not a match (need ≥ {int(pass_threshold*100)}%)"
+    )
+    score = f"Similarity: {ratio*100:.1f}%"
+
+    return hyp_raw, score, diff_html, summary
+
+with gr.Blocks(title="Say the Sentence") as demo:
+    gr.Markdown(
+        """
+        # 🎤 Say the Sentence
+        1) Generate a sentence.  
+        2) Press the mic to record yourself reading it.  
+        3) Transcribe & check.  
+        """
+    )
+
+    with gr.Row():
+        target = gr.Textbox(label="Target sentence", interactive=False, placeholder="Click 'Generate sentence'")
+    with gr.Row():
+        btn_gen = gr.Button("🎲 Generate sentence", variant="primary")
+        btn_clear = gr.Button("🧹 Clear")
+
+    with gr.Row():
+        audio = gr.Audio(sources=["microphone"], type="filepath", label="Record your voice")
+    with gr.Accordion("Advanced settings", open=False):
+        model_id = gr.Dropdown(
+            choices=[
+                "openai/whisper-tiny.en",      # Fastest (English)
+                "openai/whisper-base.en",
+                "openai/whisper-small.en",
+                "distil-whisper/distil-small.en",  # Distil variant (English)
+                "openai/whisper-tiny",         # Multilingual tiny
+            ],
+            value="openai/whisper-tiny.en",
+            label="ASR model",
+        )
+        lang = gr.Textbox(value="en", label="Language hint (e.g., 'en', 'de', 'fr')", info="Whisper language code; leave as 'en' for English-only models.")
+        device_pref = gr.Radio(choices=["auto", "cpu", "cuda"], value="auto", label="Device preference")
+        pass_threshold = gr.Slider(0.50, 1.00, value=0.85, step=0.01, label="Match threshold")
+
+    with gr.Row():
+        btn_check = gr.Button("✅ Transcribe & Check", variant="primary")
+
+    with gr.Row():
+        hyp_out = gr.Textbox(label="Transcription", interactive=False)
+    with gr.Row():
+        score_out = gr.Label(label="Score")
+        summary_out = gr.Label(label="Result")
+    diff_out = gr.HTML(label="Word-level diff (red = expected but missing / green = extra or replacement)")
+
+    # Events
+    btn_gen.click(fn=gen_sentence, outputs=target)
+    btn_clear.click(fn=lambda: ("", "", "", "", ""), outputs=[target, hyp_out, score_out, diff_out, summary_out])
+    btn_check.click(
+        fn=check_pronunciation,
+        inputs=[audio, target, model_id, lang, device_pref, pass_threshold],
+        outputs=[hyp_out, score_out, diff_out, summary_out]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

packages.txt

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+ffmpeg

requirements.txt

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+gradio>=4.39.0
+transformers>=4.44.0
+torch>=2.2.0
+accelerate>=0.33.0
+sentencepiece>=0.2.0