Update app.py

app.py

@@ -1,9 +1,16 @@
 # app.py
 
+import os
+import time
+import datetime as dt
+import pandas as pd
 import gradio as gr
 from transformers import pipeline
 import numpy as np
 import librosa  # pip install librosa
+from jiwer import wer  # pip install jiwer
+
+LOG_PATH = "feedback_logs.csv"
 
 # --- EDIT THIS: map display names to your HF Hub model IDs ---
 language_models = {
@@ -12,34 +19,30 @@ language_models = {
     "Kiswahili":                "FarmerlineML/w2v-bert-2.0_swahili_alpha",
     "Luganda":                  "FarmerlineML/w2v-bert-2.0_luganda",
     "Brazilian Portuguese":     "FarmerlineML/w2v-bert-2.0_brazilian_portugese_alpha",
-    "FANTE":                    "misterkissi/w2v2-lg-xls-r-300m-fante", 
-    "BEMBA":                    "DarliAI/kissi-w2v2-lg-xls-r-300m-bemba",
-    "BAMBARA":                  "DarliAI/kissi-w2v2-lg-xls-r-300m-bambara",
-    "DAGAARE":                  "DarliAI/kissi-w2v2-lg-xls-r-300m-dagaare",
-    "KINYARWANDA":              "DarliAI/kissi-w2v2-lg-xls-r-300m-kinyarwanda",
-    "FULA":                     "DarliAI/kissi-wav2vec2-fula-fleurs-full",
-    "OROMO":                    "DarliAI/kissi-w2v-bert-2.0-oromo",
-    "RUNYANKORE":               "misterkissi/w2v2-lg-xls-r-300m-runyankore",
-    "GA":                       "misterkissi/w2v2-lg-xls-r-300m-ga",
-    "VAI":                      "misterkissi/whisper-small-vai",
-    "KASEM":                    "misterkissi/w2v2-lg-xls-r-300m-kasem",
-    "LINGALA":                  "misterkissi/w2v2-lg-xls-r-300m-lingala",
-    "FONGBE":                   "misterkissi/whisper-small-fongbe",
-    "AMHARIC":                  "misterkissi/w2v2-lg-xls-r-1b-amharic",
-    "XHOSA":                    "misterkissi/w2v2-lg-xls-r-300m-xhosa",
-    "TSONGA":                   "misterkissi/w2v2-lg-xls-r-300m-tsonga",
-    # "WOLOF":                    "misterkissi/w2v2-lg-xls-r-1b-wolof",
-    # "HAITIAN CREOLE":           "misterkissi/whisper-small-haitian-creole",
-    # "KABYLE":                   "misterkissi/w2v2-lg-xls-r-1b-kabyle",
+    "Fante":                    "misterkissi/w2v2-lg-xls-r-300m-fante", 
+    "Bemba":                    "DarliAI/kissi-w2v2-lg-xls-r-300m-bemba",
+    "Bambara":                  "DarliAI/kissi-w2v2-lg-xls-r-300m-bambara",
+    "Dagaare":                  "DarliAI/kissi-w2v2-lg-xls-r-300m-dagaare",
+    "Kinyarwanda":              "DarliAI/kissi-w2v2-lg-xls-r-300m-kinyarwanda",
+    "Fula":                     "DarliAI/kissi-wav2vec2-fula-fleurs-full",
+    "Oromo":                    "DarliAI/kissi-w2v-bert-2.0-oromo",
+    "Runynakore":               "misterkissi/w2v2-lg-xls-r-300m-runyankore",
+    "Ga":                       "misterkissi/w2v2-lg-xls-r-300m-ga",
+    "Vai":                      "misterkissi/whisper-small-vai",
+    "Kasem":                    "misterkissi/w2v2-lg-xls-r-300m-kasem",
+    "Lingala":                  "misterkissi/w2v2-lg-xls-r-300m-lingala",
+    "Fongbe":                   "misterkissi/whisper-small-fongbe",
+    "Amharic":                  "misterkissi/w2v2-lg-xls-r-1b-amharic",
+    "Xhosa":                    "misterkissi/w2v2-lg-xls-r-300m-xhosa",
+    "Tsonga":                   "misterkissi/w2v2-lg-xls-r-300m-tsonga",
     "Yoruba":                   "FarmerlineML/w2v-bert-2.0_yoruba_v1",
-    "Luganda":                  "FarmerlineML/luganda_fkd",
+    "Luganda (FKD)":            "FarmerlineML/luganda_fkd",
     "Luo":                      "FarmerlineML/w2v-bert-2.0_luo_v2",
     "Somali":                   "FarmerlineML/w2v-bert-2.0_somali_alpha",
     "Pidgin":                   "FarmerlineML/pidgin_nigerian",
     "Kikuyu":                   "FarmerlineML/w2v-bert-2.0_kikuyu",
-    "Igbo":                     "FarmerlineML/w2v-bert-2.0_igbo_v1" 
-    
-    # add more as needed
+    "Igbo":                     "FarmerlineML/w2v-bert-2.0_igbo_v1",
+    "Krio":                     "FarmerlineML/w2v-bert-2.0_krio_v3"
 }
 
 # Pre-load pipelines for each language on CPU (device=-1)
@@ -53,53 +56,194 @@ asr_pipelines = {
     for lang, model_id in language_models.items()
 }
 
-
-def transcribe(audio_path: str, language: str) -> str:
+def transcribe(audio_path: str, language: str):
     """
     Load the audio via librosa (supports mp3, wav, flac, m4a, ogg, etc.),
     convert to mono, then run it through the chosen ASR pipeline.
+    Returns (transcript, runtime_seconds, duration_seconds).
     """
     if not audio_path:
-        return "⚠️ Please upload or record an audio clip."
+        return "⚠️ Please upload or record an audio clip.", 0.0, 0.0
 
     # librosa.load returns a 1D np.ndarray (mono) and the sample rate
     speech, sr = librosa.load(audio_path, sr=None, mono=True)
+    duration_s = librosa.get_duration(y=speech, sr=sr)
 
-    # Call the Hugging Face ASR pipeline
+    t0 = time.time()
     result = asr_pipelines[language]({
         "sampling_rate": sr,
         "raw": speech
     })
-    return result.get("text", "")
+    runtime_s = time.time() - t0
+    text = result.get("text", "")
+    return text, round(runtime_s, 3), round(duration_s, 3)
+
+def compute_wer(pred: str, ref: str) -> float:
+    if not ref or not pred:
+        return None
+    try:
+        return float(wer(ref, pred))
+    except Exception:
+        return None
+
+def ensure_logfile():
+    if not os.path.exists(LOG_PATH):
+        pd.DataFrame(columns=[
+            "timestamp", "language", "model_id", "audio_filename",
+            "duration_s", "runtime_s", "transcript", "reference",
+            "wer", "score_10", "feedback",
+            "domain", "environment", "accent_locale"
+        ]).to_csv(LOG_PATH, index=False)
+
+def save_feedback(language: str,
+                  transcript: str,
+                  reference: str,
+                  score_10: int,
+                  feedback: str,
+                  audio_file: str,
+                  duration_s: float,
+                  runtime_s: float,
+                  domain: str,
+                  environment: str,
+                  accent_locale: str):
+    ensure_logfile()
+    model_id = language_models.get(language, "")
+    audio_filename = os.path.basename(audio_file) if audio_file else ""
+
+    w = compute_wer(transcript, reference)
+
+    row = {
+        "timestamp": dt.datetime.utcnow().isoformat(),
+        "language": language,
+        "model_id": model_id,
+        "audio_filename": audio_filename,
+        "duration_s": duration_s,
+        "runtime_s": runtime_s,
+        "transcript": transcript,
+        "reference": reference,
+        "wer": w,
+        "score_10": score_10,
+        "feedback": feedback,
+        "domain": domain,
+        "environment": environment,
+        "accent_locale": accent_locale
+    }
+    try:
+        df = pd.read_csv(LOG_PATH)
+        df = pd.concat([df, pd.DataFrame([row])], ignore_index=True)
+        df.to_csv(LOG_PATH, index=False)
+        msg = "✅ Feedback saved."
+        if w is not None:
+            msg += f" WER: {w:.3f}"
+        return msg
+    except Exception as e:
+        return f"❌ Could not save feedback: {e}"
+
+def load_metrics():
+    ensure_logfile()
+    df = pd.read_csv(LOG_PATH)
+    if df.empty:
+        return "No feedback yet.", None, None, df
 
+    # Aggregates
+    # Per-language means:
+    per_lang = df.groupby("language").agg(
+        n=("wer", "count"),
+        mean_WER=("wer", "mean"),
+        mean_score=("score_10", "mean"),
+        mean_runtime_s=("runtime_s", "mean"),
+        mean_duration_s=("duration_s", "mean")
+    ).reset_index().sort_values(by="mean_WER", ascending=True)
 
-with gr.Blocks(title="🌐 Multilingual ASR Demo") as demo:
+    # Per-domain (optional):
+    per_domain = df.groupby("domain").agg(
+        n=("wer", "count"),
+        mean_WER=("wer", "mean"),
+        mean_score=("score_10", "mean")
+    ).reset_index().sort_values(by="mean_WER", ascending=True)
+
+    return "📊 Metrics updated.", per_lang, per_domain, df
+
+with gr.Blocks(title="🌐 Multilingual ASR Demo", theme=gr.themes.Soft()) as demo:
     gr.Markdown(
         """
-        ## 🎙️ Multilingual Speech-to-Text   
+        ## 🎙️ Multilingual Speech-to-Text + Feedback & Benchmarking
         Upload an audio file (MP3, WAV, FLAC, M4A, OGG,…) or record via your microphone.  
-        Then choose the language/model and hit **Transcribe**.
+        Choose the language/model and hit **Transcribe**.  
+        Optionally provide a **reference transcript** to compute WER, then leave a score & feedback.
         """
     )
 
-    with gr.Row():
-        lang = gr.Dropdown(
-            choices=list(language_models.keys()),
-            value=list(language_models.keys())[0],
-            label="Select Language / Model"
-        )
+    with gr.Tabs():
+        with gr.Tab("ASR"):
+            with gr.Row():
+                lang = gr.Dropdown(
+                    choices=list(language_models.keys()),
+                    value=list(language_models.keys())[0],
+                    label="Select Language / Model"
+                )
+
+            with gr.Row():
+                audio = gr.Audio(
+                    sources=["upload", "microphone"],
+                    type="filepath",
+                    label="Upload or Record Audio"
+                )
+
+            btn = gr.Button("Transcribe", variant="primary")
+            output = gr.Textbox(label="Transcription", lines=6)
+            runtime = gr.Number(label="Model runtime (s)", precision=3, interactive=False)
+            duration = gr.Number(label="Audio duration (s)", precision=3, interactive=False)
+
+            # Feedback / Benchmark block
+            gr.Markdown("### 📝 Feedback & WER (optional)")
+            with gr.Row():
+                reference = gr.Textbox(label="Reference transcript (optional, for WER)", lines=4, placeholder="Paste the ground-truth text here to compute WER")
+            with gr.Row():
+                score = gr.Slider(0, 10, step=1, value=8, label="Overall quality score (0–10)")
+            with gr.Row():
+                domain = gr.Dropdown(
+                    ["General", "Conversational", "News", "Agriculture", "Healthcare", "Education", "Customer support", "Finance", "Legal", "Entertainment", "Other"],
+                    value="General",
+                    label="Domain/topic"
+                )
+                environment = gr.Dropdown(
+                    ["Quiet", "Office", "Outdoor", "Vehicle", "Crowd/Market", "Radio/Phone", "Other"],
+                    value="Quiet",
+                    label="Recording environment"
+                )
+                accent_locale = gr.Textbox(label="Accent / Locale (e.g., Accra, Nairobi, Lagos)", placeholder="Optional")
+
+            feedback = gr.Textbox(label="Free-text feedback", lines=4, placeholder="What worked well? What failed? Any specific words or sounds?")
+
+            save_btn = gr.Button("Save Feedback", variant="secondary")
+            save_msg = gr.Markdown("")
+
+            # Wire up
+            btn.click(
+                fn=transcribe,
+                inputs=[audio, lang],
+                outputs=[output, runtime, duration]
+            )
 
-    with gr.Row():
-        audio = gr.Audio(
-            sources=["upload", "microphone"],
-            type="filepath",
-            label="Upload or Record Audio"
-        )
+            save_btn.click(
+                fn=save_feedback,
+                inputs=[lang, output, reference, score, feedback, audio, duration, runtime, domain, environment, accent_locale],
+                outputs=save_msg
+            )
 
-    btn = gr.Button("Transcribe")
-    output = gr.Textbox(label="Transcription")
+        with gr.Tab("Metrics"):
+            refresh = gr.Button("Refresh metrics", variant="primary")
+            metrics_msg = gr.Markdown()
+            per_lang_df = gr.Dataframe(interactive=False, label="Per-language summary (lower WER is better)")
+            per_domain_df = gr.Dataframe(interactive=False, label="Per-domain summary")
+            logs_df = gr.Dataframe(interactive=False, label="Raw feedback log")
 
-    btn.click(fn=transcribe, inputs=[audio, lang], outputs=output)
+            refresh.click(
+                fn=load_metrics,
+                inputs=[],
+                outputs=[metrics_msg, per_lang_df, per_domain_df, logs_df]
+            )
 
 if __name__ == "__main__":
     demo.launch()