Explainations of demo.md

demo.md

@@ -65,58 +65,6 @@ def evaluate_model(ds, pipe, wer_metric):
 # Load WER metric
 wer_metric = evaluate.load("wer")
 
-results = {}
-model_wer_results = {}
-# Evaluate model
-for model in model_name:
-    pipe = pipeline("automatic-speech-recognition", model=model_name[model])
-    wer_scores, wer_results = evaluate_model(ds, pipe, wer_metric)
-    results[model] = np.mean(wer_scores)
-    model_wer_results[model] = wer_results
-
-for model in results:
-    print(f"Model: {model}, WER: {results[model]}")
-```
-
-## Whole script
-
-```python
-from datasets import load_dataset
-from transformers import pipeline
-import evaluate
-import numpy as np
-from tqdm import tqdm
-
-ds = load_dataset("openslr/librispeech_asr", "clean", split="validation", streaming=True)
-ds = ds.take(100)
-
-model_name = {
-    "whisper-tiny": "openai/whisper-tiny.en",
-    "wav2vec2-large-960h": "facebook/wav2vec2-base-960h",
-    "distill-whisper-small": "distil-whisper/distil-small.en",
-}
-
-def evaluate_model(ds, pipe, wer_metric):
-    wer_scores = []
-    wer_results = []
-    for idx, sample in enumerate(tqdm(ds, desc="Evaluating", total=len(list(ds)))):
-        audio_sample = sample["audio"]
-        transcription = pipe(audio_sample["array"])['text']
-        # Remove , and . from the transcription
-        transcription = transcription.replace(",", "").replace(".", "")
-        wer = wer_metric.compute(predictions=[transcription.upper()], references=[sample["text"].upper()])
-        wer_scores.append(wer)
-        wer_results.append({
-            "index": idx,
-            "transcription": transcription.upper(),
-            "reference": sample["text"].upper(),
-            "wer": wer
-        })
-    return wer_scores, wer_results
-
-# Load WER metric
-wer_metric = evaluate.load("wer")
-
 results = {}
 model_wer_results = {}
 # Evaluate model