David Pomerenke
commited on
Commit
Β·
80a0827
1
Parent(s):
b1da19b
Run on 50 languages
Browse files- app.py +5 -4
- evals.py +12 -7
- results.json +710 -300
app.py
CHANGED
@@ -137,17 +137,18 @@ def create_leaderboard_df(model_type, metric=None):
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"model_": "Model",
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}
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)
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df = df.sort_values(metric["label"], ascending=False)
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df["Rank"] = range(1, len(df) + 1)
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df["Rank"] = df["Rank"].apply(
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lambda x: "π₯" if x == 1 else "π₯" if x == 2 else "π₯" if x == 3 else str(x)
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)
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-
df = df[["Rank", "Model", metric["label"]
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return gr.DataFrame(
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value=df,
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label="Model Leaderboard",
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show_search=False,
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-
datatype=["number", "markdown", "number"
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)
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@@ -321,7 +322,7 @@ def create_scatter_plot(metric):
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sorted(set(data["family"] for data in scatter_data)), color_pallette
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)
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}
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-
c_vals = [color_mapping
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labels = [data["language"] for data in scatter_data]
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hover_template = f"<b>%{{text}}</b><br>Speakers: %{{x:.1f}}M<br>{metric['label']}: %{{y:.3f}}<extra></extra>"
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fig.add_trace(
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@@ -447,7 +448,7 @@ def create_world_map(metric):
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hover_texts = []
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for country_code, data in country_data.items():
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-
weighted_avg = data["weighted_score_sum"] / data["total_speakers"]
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try:
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country_name = pycountry.countries.get(alpha_3=country_code).name
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"model_": "Model",
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}
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)
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+
df[metric["label"]] = df[metric["label"]].round(3)
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df = df.sort_values(metric["label"], ascending=False)
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df["Rank"] = range(1, len(df) + 1)
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df["Rank"] = df["Rank"].apply(
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lambda x: "π₯" if x == 1 else "π₯" if x == 2 else "π₯" if x == 3 else str(x)
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)
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+
df = df[["Rank", "Model", metric["label"]]]
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return gr.DataFrame(
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value=df,
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label="Model Leaderboard",
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show_search=False,
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+
datatype=["number", "markdown", "number"],
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)
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sorted(set(data["family"] for data in scatter_data)), color_pallette
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)
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}
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+
c_vals = [color_mapping.get(data["family"], "LightGray") for data in scatter_data]
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labels = [data["language"] for data in scatter_data]
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hover_template = f"<b>%{{text}}</b><br>Speakers: %{{x:.1f}}M<br>{metric['label']}: %{{y:.3f}}<extra></extra>"
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fig.add_trace(
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hover_texts = []
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for country_code, data in country_data.items():
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+
weighted_avg = data["weighted_score_sum"] / data["total_speakers"] if data["total_speakers"] > 0 else None
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try:
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country_name = pycountry.countries.get(alpha_3=country_code).name
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evals.py
CHANGED
@@ -36,9 +36,13 @@ models = [
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"google/gemini-2.0-flash-001", # 0.4$/M tokens
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# "qwen/qwen-turbo", # 0.2$/M tokens; recognizes "inappropriate content"
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# "deepseek/deepseek-chat", # 0.9$/M tokens
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-
"microsoft/phi-4", # 0.07$/M tokens; only 16k tokens context
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]
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model_fast = "meta-llama/llama-3.3-70b-instruct"
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transcription_models = [
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"elevenlabs/scribe_v1",
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"openai/whisper-large-v3",
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@@ -46,9 +50,8 @@ transcription_models = [
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# "facebook/seamless-m4t-v2-large",
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]
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transcription_model_fast = "elevenlabs/scribe_v1"
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-
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-
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-
n_detailed_languages = 5
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# ===== setup =====
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@@ -195,6 +198,8 @@ target_languages = languages[languages["in_benchmark"]].sample(
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)
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langs_eval = languages.iloc[:n_languages]
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langs_eval_detailed = languages.iloc[:n_detailed_languages]
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def download_file(url, path):
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@@ -205,7 +210,7 @@ def download_file(url, path):
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def download_fleurs():
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# the huggingface loader does not allow loading only the dev set, so do it manually
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-
for language in
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tar_url = f"https://huggingface.co/datasets/google/fleurs/resolve/main/data/{language.fleurs_tag}/audio/dev.tar.gz"
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tar_path = Path(f"data/fleurs/{language.fleurs_tag}/audio/dev.tar.gz")
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audio_path = Path(f"data/fleurs/{language.fleurs_tag}/audio")
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@@ -496,12 +501,12 @@ async def main():
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transcription_scores = [
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transcribe_and_evaluate(model, language.bcp_47, i)
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for i in range(n_sentences)
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-
for language in
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for model in transcription_models
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if language.in_benchmark
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and (
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model == transcription_model_fast
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-
or language.bcp_47 in
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)
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]
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transcription_scores = await tqdm_asyncio.gather(*transcription_scores, miniters=1)
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"google/gemini-2.0-flash-001", # 0.4$/M tokens
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# "qwen/qwen-turbo", # 0.2$/M tokens; recognizes "inappropriate content"
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# "deepseek/deepseek-chat", # 0.9$/M tokens
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+
# "microsoft/phi-4", # 0.07$/M tokens; only 16k tokens context
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]
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model_fast = "meta-llama/llama-3.3-70b-instruct"
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+
n_languages = 50
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+
n_detailed_languages = 10
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+
n_sentences = 30
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+
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transcription_models = [
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"elevenlabs/scribe_v1",
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"openai/whisper-large-v3",
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# "facebook/seamless-m4t-v2-large",
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]
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transcription_model_fast = "elevenlabs/scribe_v1"
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+
transcription_n_languages = 10
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+
transcription_n_detailed_languages = 5
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# ===== setup =====
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)
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langs_eval = languages.iloc[:n_languages]
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langs_eval_detailed = languages.iloc[:n_detailed_languages]
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+
transcription_langs_eval = languages.iloc[:transcription_n_languages]
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+
transcription_langs_eval_detailed = languages.iloc[:transcription_n_detailed_languages]
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def download_file(url, path):
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def download_fleurs():
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# the huggingface loader does not allow loading only the dev set, so do it manually
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+
for language in transcription_langs_eval.itertuples():
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tar_url = f"https://huggingface.co/datasets/google/fleurs/resolve/main/data/{language.fleurs_tag}/audio/dev.tar.gz"
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tar_path = Path(f"data/fleurs/{language.fleurs_tag}/audio/dev.tar.gz")
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audio_path = Path(f"data/fleurs/{language.fleurs_tag}/audio")
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transcription_scores = [
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transcribe_and_evaluate(model, language.bcp_47, i)
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for i in range(n_sentences)
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+
for language in transcription_langs_eval.itertuples()
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for model in transcription_models
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if language.in_benchmark
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and (
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model == transcription_model_fast
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+
or language.bcp_47 in transcription_langs_eval_detailed.bcp_47.values
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)
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]
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transcription_scores = await tqdm_asyncio.gather(*transcription_scores, miniters=1)
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results.json
CHANGED
@@ -40,15 +40,6 @@
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"mlm_chrf": 0.9820612175447262,
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"t2t_score": 0.819029567439605
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},
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-
{
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-
"model": "microsoft/phi-4",
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-
"model_type": "text-to-text",
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-
"mt_bleu": 0.37572897166941227,
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-
"mt_chrf": 0.5070735300311086,
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-
"cls_acc": 0.6666666666666666,
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-
"mlm_chrf": 0.969030413937307,
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-
"t2t_score": 0.714256870211694
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-
},
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{
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"model": "elevenlabs/scribe_v1",
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"model_type": "speech-to-text",
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@@ -224,13 +215,13 @@
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"ZW": 6109446
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},
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"language_family": "Indo-European",
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-
"mt_bleu": 0.
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-
"mt_chrf": 0.
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-
"cls_acc": 0.
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-
"mlm_chrf": 0.
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"asr_wer": 0.2883431971452567,
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"asr_chrf": 0.8410097119423834,
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-
"t2t_score": 0.
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"s2t_score": 0.5646764545438201
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},
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{
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@@ -274,15 +265,6 @@
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"mlm_chrf": 0.9353777720326482,
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"t2t_score": 0.7876025558161427
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},
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-
{
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-
"model": "microsoft/phi-4",
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-
"model_type": "text-to-text",
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-
"mt_bleu": 0.3516110848479108,
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-
"mt_chrf": 0.5536738005415017,
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-
"cls_acc": 0.7333333333333333,
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-
"mlm_chrf": 0.9080915180096581,
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-
"t2t_score": 0.7316995506281644
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-
},
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{
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"model": "elevenlabs/scribe_v1",
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"model_type": "speech-to-text",
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@@ -323,13 +305,13 @@
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"VN": 1085934
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},
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"language_family": "Sino-Tibetan",
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-
"mt_bleu": 0.
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-
"mt_chrf": 0.
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-
"cls_acc": 0.
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-
"mlm_chrf": 0.
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"asr_wer": 1.0,
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"asr_chrf": 0.7412819691487745,
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-
"t2t_score": 0.
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"s2t_score": 0.8706409845743872
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},
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{
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@@ -373,15 +355,6 @@
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"mlm_chrf": 0.9648559476590244,
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"t2t_score": 0.7852166232038682
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},
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-
{
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-
"model": "microsoft/phi-4",
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-
"model_type": "text-to-text",
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-
"mt_bleu": 0.27124364337229007,
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-
"mt_chrf": 0.43849820754378105,
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-
"cls_acc": 0.8,
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-
"mlm_chrf": 0.9387109966783314,
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-
"t2t_score": 0.7257364014073708
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-
},
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{
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"model": "elevenlabs/scribe_v1",
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"model_type": "speech-to-text",
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@@ -408,13 +381,13 @@
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"ZA": 1129272
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},
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"language_family": "Indo-European",
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-
"mt_bleu": 0.
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-
"mt_chrf": 0.
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-
"cls_acc": 0.
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-
"mlm_chrf": 0.
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"asr_wer": 0.3581497443457955,
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"asr_chrf": 0.7041899877791161,
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-
"t2t_score": 0.
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"s2t_score": 0.5311698660624558
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},
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{
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@@ -458,15 +431,6 @@
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"mlm_chrf": 0.9816202595213187,
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"t2t_score": 0.7836003741246967
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},
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-
{
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-
"model": "microsoft/phi-4",
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-
"model_type": "text-to-text",
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-
"mt_bleu": 0.2422835449771352,
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-
"mt_chrf": 0.4339464294262581,
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-
"cls_acc": 0.7666666666666667,
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-
"mlm_chrf": 0.9650899128314744,
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-
"t2t_score": 0.7219010029747998
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-
},
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{
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"model": "elevenlabs/scribe_v1",
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"model_type": "speech-to-text",
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@@ -526,13 +490,13 @@
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"VE": 23488572
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},
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"language_family": "Indo-European",
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-
"mt_bleu": 0.
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-
"mt_chrf": 0.
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-
"cls_acc": 0.
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-
"mlm_chrf": 0.
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"asr_wer": 0.18417222540761574,
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"asr_chrf": 0.9063759698635353,
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-
"t2t_score": 0.
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"s2t_score": 0.5452740976355754
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},
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{
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@@ -576,15 +540,6 @@
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"mlm_chrf": 0.97921999148367,
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"t2t_score": 0.7974861134300695
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},
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-
{
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-
"model": "microsoft/phi-4",
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-
"model_type": "text-to-text",
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-
"mt_bleu": 0.2419401543819311,
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-
"mt_chrf": 0.4175700747114916,
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-
"cls_acc": 0.7,
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-
"mlm_chrf": 0.9332394461837199,
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-
"t2t_score": 0.6836031736317372
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-
},
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{
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"model": "elevenlabs/scribe_v1",
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"model_type": "speech-to-text",
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@@ -643,13 +598,13 @@
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"YE": 22114456
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},
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"language_family": "Afro-Asiatic",
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-
"mt_bleu": 0.
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-
"mt_chrf": 0.
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-
"cls_acc": 0.
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-
"mlm_chrf": 0.
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"asr_wer": 0.23569148982352306,
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"asr_chrf": 0.8475974658647911,
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-
"t2t_score": 0.
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"s2t_score": 0.5416444778441571
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},
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{
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"bcp_47": "ur",
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"speakers": 290790290,
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"scores": [
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{
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"model": "meta-llama/llama-3.3-70b-instruct",
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"model_type": "text-to-text",
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@@ -666,6 +630,24 @@
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"mlm_chrf": 0.9330700955297437,
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"t2t_score": 0.6011143080087092
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},
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{
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"model": "elevenlabs/scribe_v1",
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"model_type": "speech-to-text",
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@@ -684,13 +666,13 @@
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"PK": 221825950
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},
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"language_family": "Indo-European",
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-
"mt_bleu": 0.
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-
"mt_chrf": 0.
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-
"cls_acc": 0.
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-
"mlm_chrf": 0.
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"asr_wer": 0.33586677704198,
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"asr_chrf": 0.7430938264813908,
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-
"t2t_score": 0.
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"s2t_score": 0.5394803017616854
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},
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{
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"bcp_47": "fr",
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"speakers": 278611507,
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"scores": [
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{
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"model": "meta-llama/llama-3.3-70b-instruct",
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"model_type": "text-to-text",
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"mlm_chrf": 0.9639910578331403,
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"t2t_score": 0.6753073934678575
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},
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{
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"model": "elevenlabs/scribe_v1",
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"model_type": "speech-to-text",
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"YT": 110580
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},
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"language_family": "Indo-European",
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-
"mt_bleu": 0.
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"mt_chrf": 0.
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"cls_acc": 0.
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-
"mlm_chrf": 0.
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"asr_wer": 0.2610754929736961,
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"asr_chrf": 0.8775590287945104,
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-
"t2t_score": 0.
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"s2t_score": 0.5693172608841033
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},
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{
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"bcp_47": "bn",
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"speakers": 267193288,
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"scores": [
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{
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"model": "meta-llama/llama-3.3-70b-instruct",
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"model_type": "text-to-text",
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"mlm_chrf": 0.8995877938471141,
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"t2t_score": 0.5622979523454997
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},
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8812 |
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8913 |
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