add

app.py

@@ -1,3 +1,5 @@
+"""
+
 import gradio as gr
 import torch
 import torchaudio
@@ -40,3 +42,4 @@ iface = gr.Interface(
 if __name__ == "__main__":
     iface.launch()
 
+"""

sample.py

@@ -0,0 +1,112 @@
+import os
+import time
+import torch
+import torchaudio
+import gradio as gr
+from transformers import (
+    Wav2Vec2Processor, HubertForCTC,
+    WhisperProcessor, WhisperForConditionalGeneration
+)
+from phonemizer import phonemize
+import difflib
+
+# === Setup: Load all 3 models ===
+
+# 1. Base HuBERT
+base_proc = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft")
+base_model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft").eval()
+
+# 2. Whisper + phonemizer
+whisper_proc = WhisperProcessor.from_pretrained("openai/whisper-base")
+whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base").eval()
+
+# 3. My Hubert Model
+token = os.environ.get("HF_TOKEN") 
+your_proc = Wav2Vec2Processor.from_pretrained("tecasoftai/hubert-finetune", token=token)
+your_model = HubertForCTC.from_pretrained("tecasoftai/hubert-finetune", token=token).eval()
+
+# === Helper ===
+
+def load_audio(filepath):
+    waveform, sr = torchaudio.load(filepath)
+    if sr != 16000:
+        waveform = torchaudio.functional.resample(waveform, sr, 16000)
+    return waveform.squeeze()
+
+def calc_per(pred, ref):
+    pred_list = pred.strip().split()
+    ref_list = ref.strip().split()
+    sm = difflib.SequenceMatcher(None, ref_list, pred_list)
+    dist = sum(tr[-1] for tr in sm.get_opcodes() if tr[0] != 'equal')
+    if len(ref_list) == 0:
+        return 0.0
+    return round(100 * dist / len(ref_list), 2)
+
+# === Inference functions ===
+
+def run_hubert_base(wav):
+    start = time.time()
+    inputs = base_proc(wav, sampling_rate=16000, return_tensors="pt")
+    with torch.no_grad():
+        logits = base_model(**inputs).logits
+    ids = torch.argmax(logits, dim=-1)
+    phonemes = base_proc.batch_decode(ids)[0]
+    return phonemes, time.time() - start
+
+def run_whisper(wav):
+    start = time.time()
+    inputs = whisper_proc(wav, sampling_rate=16000, return_tensors="pt")
+    with torch.no_grad():
+        ids = whisper_model.generate(inputs["input_features"])
+    text = whisper_proc.batch_decode(ids, skip_special_tokens=True)[0]
+    phonemes = phonemize(text, language='en-us', backend='espeak')
+    return phonemes, time.time() - start
+
+def run_your_model(wav):
+    start = time.time()
+    inputs = your_proc(wav, sampling_rate=16000, return_tensors="pt")
+    with torch.no_grad():
+        logits = your_model(**inputs).logits
+    ids = torch.argmax(logits, dim=-1)
+    phonemes = your_proc.batch_decode(ids)[0]
+    return phonemes, time.time() - start
+
+# === Main Gradio function ===
+
+def benchmark_all(audio_path, reference_phoneme):
+    wav = load_audio(audio_path)
+
+    results = []
+
+    # 1. HuBERT Base
+    phonemes, dur = run_hubert_base(wav)
+    per = calc_per(phonemes, reference_phoneme)
+    results.append(["HuBERT-Base", phonemes, f"{dur:.2f}s", f"{per}%"])
+
+    # 2. Whisper
+    phonemes, dur = run_whisper(wav)
+    per = calc_per(phonemes, reference_phoneme)
+    results.append(["Whisper + Phonemizer", phonemes, f"{dur:.2f}s", f"{per}%"])
+
+    # 3. My Hubert model
+    phonemes, dur = run_your_model(wav)
+    per = calc_per(phonemes, reference_phoneme)
+    results.append(["Your HuBERT (fine-tuned)", phonemes, f"{dur:.2f}s", f"{per}%"])
+
+    return results
+
+# === UI ===
+
+demo = gr.Interface(
+    fn=benchmark_all,
+    inputs=[
+        gr.Audio(type="filepath", label="Upload Audio"),
+        gr.Textbox(label="Ground-truth Phonemes (space-separated)", placeholder="f ə n ə m aɪ z")
+    ],
+    outputs=gr.Dataframe(headers=["Model", "Phoneme Output", "Inference Time", "PER (%)"]),
+    title="Phoneme Recognition Benchmark",
+    description="Compare HuBERT-Base, Whisper, and your fine-tuned model on phoneme recognition."
+)
+
+if __name__ == "__main__":
+    demo.launch()