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import os |
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import numpy as np |
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import unicodedata |
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import diff_match_patch as dmp_module |
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from enum import Enum |
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import gradio as gr |
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from datasets import load_dataset |
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import pandas as pd |
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from jiwer import process_words, wer_default |
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from nltk import ngrams |
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class Action(Enum): |
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INSERTION = 1 |
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DELETION = -1 |
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EQUAL = 0 |
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def compare_string(text1: str, text2: str) -> list: |
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text1_normalized = unicodedata.normalize("NFKC", text1) |
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text2_normalized = unicodedata.normalize("NFKC", text2) |
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dmp = dmp_module.diff_match_patch() |
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diff = dmp.diff_main(text1_normalized, text2_normalized) |
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dmp.diff_cleanupSemantic(diff) |
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return diff |
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def style_text(diff): |
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fullText = "" |
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for action, text in diff: |
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if action == Action.INSERTION.value: |
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fullText += f"<span style='background-color:Lightgreen'>{text}</span>" |
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elif action == Action.DELETION.value: |
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fullText += f"<span style='background-color:#FFCCCB'><s>{text}</s></span>" |
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elif action == Action.EQUAL.value: |
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fullText += f"{text}" |
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else: |
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raise Exception("Not Implemented") |
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fullText = fullText.replace("](", "]\(").replace("~", "\~") |
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return fullText |
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dataset = load_dataset( |
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"distil-whisper/tedlium-long-form", split="validation", num_proc=os.cpu_count() |
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) |
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csv_v2 = pd.read_csv("assets/large-v2.csv") |
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norm_target = csv_v2["Norm Target"] |
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norm_pred_v2 = csv_v2["Norm Pred"] |
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norm_target = [norm_target[i] for i in range(len(norm_target))] |
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norm_pred_v2 = [norm_pred_v2[i] for i in range(len(norm_pred_v2))] |
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csv_v2 = pd.read_csv("assets/large-32-2.csv") |
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norm_pred_32_2 = csv_v2["Norm Pred"] |
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norm_pred_32_2 = [norm_pred_32_2[i] for i in range(len(norm_pred_32_2))] |
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target_dtype = np.int16 |
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max_range = np.iinfo(target_dtype).max |
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def get_statistics(model="large-v2", round_dp=2, ngram_degree=5): |
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text1 = norm_target |
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if model == "large-v2": |
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text2 = norm_pred_v2 |
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elif model == "large-32-2": |
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text2 = norm_pred_32_2 |
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else: |
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raise ValueError( |
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f"Got unknown model {model}, should be one of `'large-v2'` or `'large-32-2'`." |
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) |
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wer_output = process_words(text1, text2, wer_default, wer_default) |
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wer_percentage = round(100 * wer_output.wer, round_dp) |
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ier_percentage = round( |
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100 * wer_output.insertions / sum([len(ref) for ref in wer_output.references]), round_dp |
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) |
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all_ngrams = list(ngrams(" ".join(text2).split(), ngram_degree)) |
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unique_ngrams = [] |
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for ngram in all_ngrams: |
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if ngram not in unique_ngrams: |
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unique_ngrams.append(ngram) |
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repeated_ngrams = len(all_ngrams) - len(unique_ngrams) |
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return wer_percentage, ier_percentage, repeated_ngrams |
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def get_overall_table(): |
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large_v2 = get_statistics(model="large-v2") |
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large_32_2 = get_statistics(model="large-32-2") |
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table = [large_v2, large_32_2] |
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table[0] = ["Whisper", *table[0]] |
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table[1] = ["Distil-Whisper", *table[1]] |
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return table |
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def get_visualisation(idx, model="large-v2", round_dp=2, ngram_degree=5): |
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idx -= 1 |
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audio = dataset[idx]["audio"] |
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array = (audio["array"] * max_range).astype(np.int16) |
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sampling_rate = audio["sampling_rate"] |
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text1 = norm_target[idx] |
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if model == "large-v2": |
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text2 = norm_pred_v2[idx] |
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elif model == "large-32-2": |
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text2 = norm_pred_32_2[idx] |
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else: |
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raise ValueError( |
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f"Got unknown model {model}, should be one of `'large-v2'` or `'large-32-2'`." |
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) |
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wer_output = process_words(text1, text2, wer_default, wer_default) |
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wer_percentage = round(100 * wer_output.wer, round_dp) |
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ier_percentage = round( |
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100 * wer_output.insertions / len(wer_output.references[0]), round_dp |
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) |
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all_ngrams = list(ngrams(text2.split(), ngram_degree)) |
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unique_ngrams = [] |
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for ngram in all_ngrams: |
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if ngram not in unique_ngrams: |
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unique_ngrams.append(ngram) |
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repeated_ngrams = len(all_ngrams) - len(unique_ngrams) |
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diff = compare_string(text1, text2) |
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full_text = style_text(diff) |
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return ( |
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(sampling_rate, array), |
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wer_percentage, |
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ier_percentage, |
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repeated_ngrams, |
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full_text, |
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) |
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def get_side_by_side_visualisation(idx): |
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large_v2 = get_visualisation(idx, model="large-v2") |
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large_32_2 = get_visualisation(idx, model="large-32-2") |
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table = [large_v2[1:-1], large_32_2[1:-1]] |
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table[0] = ["Whisper", *table[0]] |
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table[1] = ["Distil-Whisper", *table[1]] |
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return large_v2[0], table, large_v2[-1], large_32_2[-1] |
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if __name__ == "__main__": |
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with gr.Blocks() as demo: |
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gr.HTML( |
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""" |
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<div style="text-align: center; max-width: 700px; margin: 0 auto;"> |
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<div |
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style=" |
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display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem; |
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" |
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> |
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<h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;"> |
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Whisper Transcription Analysis |
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</h1> |
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</div> |
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</div> |
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""" |
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) |
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gr.Markdown( |
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""" |
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One of the major claims of the <a href="https://arxiv.org/abs/2311.00430"> Distil-Whisper paper</a> is that |
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that Distil-Whisper hallucinates less than Whisper on long-form audio. To demonstrate this, we'll analyse the |
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transcriptions generated by <a href="https://huggingface.co/openai/whisper-large-v2"> Whisper</a> |
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and <a href="https://huggingface.co/distil-whisper/distil-large-v2"> Distil-Whisper</a> on the |
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<a href="https://huggingface.co/datasets/distil-whisper/tedlium-long-form"> TED-LIUM</a> validation set. |
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To quantify the amount of repetition and hallucination in the predicted transcriptions, we measure the number |
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of repeated 5-gram word duplicates (5-Dup.) and the insertion error rate (IER). Analysis is performed on the |
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overall level, where statistics are computed over the entire dataset, and also a per-sample level (i.e. an |
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on an individual example basis). |
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The transcriptions for both models are shown at the bottom of the demo. We compute a text difference for each |
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relative to the ground truth transcriptions. Insertions are displayed in <span style='background-color:Lightgreen'>green</span>, |
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and deletions in <span style='background-color:#FFCCCB'><s>red</s></span>. Multiple words in <span style='background-color:Lightgreen'>green</span> |
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indicates that a model has hallucinated, since it has inserted words not present in the ground truth transcription. |
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Overall, Distil-Whisper has roughly half the number of 5-Dup. and IER. This indicates that it has a lower |
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propensity to hallucinate compared to the Whisper model. Try both models with some of the TED-LIUM examples |
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and view the reduction in hallucinations for yourself! |
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""" |
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) |
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gr.Markdown("**Overall statistics:**") |
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table = gr.Dataframe( |
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value=get_overall_table(), |
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headers=[ |
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"Model", |
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"Word Error Rate (WER)", |
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"Insertion Error Rate (IER)", |
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"Repeated 5-grams", |
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], |
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row_count=2, |
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) |
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gr.Markdown("**Per-sample statistics:**") |
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slider = gr.Slider( |
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minimum=1, maximum=len(norm_target), step=1, label="Dataset sample" |
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) |
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btn = gr.Button("Analyse") |
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audio_out = gr.Audio(label="Audio input") |
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with gr.Column(): |
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table = gr.Dataframe( |
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headers=[ |
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"Model", |
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"Word Error Rate (WER)", |
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"Insertion Error Rate (IER)", |
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"Repeated 5-grams", |
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], |
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row_count=2, |
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) |
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with gr.Row(): |
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gr.Markdown("**Whisper text diff**") |
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gr.Markdown("**Distil-Whisper text diff**") |
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with gr.Row(): |
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text_out_v2 = gr.Markdown(label="Text difference") |
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text_out_32_2 = gr.Markdown(label="Text difference") |
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btn.click( |
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fn=get_side_by_side_visualisation, |
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inputs=slider, |
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outputs=[audio_out, table, text_out_v2, text_out_32_2], |
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) |
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demo.launch() |
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