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import pandas as pd
import streamlit as st
import matplotlib.pyplot as plt
import seaborn as sns
import os
import json
from utils import read_reports, dict_to_multindex_df
#add_test_split_stats_from_secret_dataset, dict_to_multindex_df_all_splits
from utils import extract_stats_to_agg, extract_stats_all_splits, extract_stats_for_dataset_card
from constants import BIGOS_INFO, PELCRA_INFO, ABOUT_INFO
from datasets import get_dataset_config_names
# PL ASR BIGOS analysis
# PL ASR Diagnostic analysis
# PELCRA analysis
# TODO - compare the datasets
st.set_page_config(layout="wide")
metrics_size_audio = ["samples", "audio[h]", "speakers"]
metrics_size_text = ["samples", "words", "chars"]
metrics_size = metrics_size_audio + metrics_size_text
metrics_features_text_uniq = ["utts_unique", "words_unique", "chars_unique"]
metrics_features_speech_rate = ["words_per_sec", "chars_per_sec"]
metrics_features_duration = ["average_audio_duration[s]", "average_utterance_length[words]", "average_utterance_length[chars]"]
metrics_features_meta = ["meta_cov_sex", "meta_cov_age"]
metrics_features = metrics_features_text_uniq + metrics_features_speech_rate + metrics_features_duration + metrics_features_meta
about, analysis_bigos, analysis_bigos_diagnostic, analysis_bigos_pelcra = st.tabs(["About BIGOS datasets", "BIGOS V2 analysis", "BIGOS V2 diagnostic", "PELCRA for BIGOS analysis"])
#analysis_bigos_diagnostic
#########################################BIGOS################################################
with about:
st.title("About BIGOS project")
st.markdown(ABOUT_INFO, unsafe_allow_html=True)
# TODO - load and display about BIGOS benchmark
with analysis_bigos:
dataset_name = "amu-cai/pl-asr-bigos-v2"
dataset_short_name = "BIGOS"
dataset_version = "V2"
dataset_configs = get_dataset_config_names(dataset_name,trust_remote_code=True)
# remove "all" subset, which is always the last config type
dataset_configs.pop()
print(dataset_configs)
# read the reports for public and secret datasets
[stats_dict_public, contents_dict_public] = read_reports(dataset_name)
# update the metrics for test split with the secret dataset statistics
#stats_dict_public = add_test_split_stats_from_secret_dataset(stats_dict_public, stats_dict_secret)
df_multindex_for_agg = dict_to_multindex_df(stats_dict_public, all_splits=False)
df_multindex_all_splits = dict_to_multindex_df(stats_dict_public, all_splits=True)
# extract metrics from dictionary and convert to various dataframes for easier analysis and visualization
#st.header("Summary statistics")
st.header("Dataset level metrics")
df_sum_stats_agg = extract_stats_to_agg(df_multindex_for_agg, metrics_size)
# split dataframe into separate dataframes for easier analysis and visualization
st.subheader("Audio content size")
df_sum_stats_audio = df_sum_stats_agg[metrics_size_audio]
st.dataframe(df_sum_stats_audio)
st.subheader("Text content size")
df_sum_stats_text = df_sum_stats_agg[metrics_size_text]
st.dataframe(df_sum_stats_text)
df_sum_stats_all_splits = extract_stats_all_splits(df_multindex_all_splits, metrics_features)
st.subheader("Utterances, vocabulary and alphabet space")
df_sum_stats_feats_text = df_sum_stats_all_splits[metrics_features_text_uniq]
st.dataframe(df_sum_stats_feats_text)
st.subheader("Speech rates")
df_sum_stats_feats_speech_rate= df_sum_stats_all_splits[metrics_features_speech_rate]
st.dataframe(df_sum_stats_feats_speech_rate)
st.subheader("Average utterance lengths and audio duration")
df_sum_stats_feats_durations = df_sum_stats_all_splits[metrics_features_duration]
st.dataframe(df_sum_stats_feats_durations)
st.subheader("Metadata coverage")
df_sum_stats_feats_meta = df_sum_stats_all_splits[metrics_features_meta]
st.dataframe(df_sum_stats_feats_meta)
st.header("BIGOS subsets (source datasets) cards")
for subset in dataset_configs:
st.subheader("Dataset card for: {}".format(subset))
df_metrics_subset_size = extract_stats_for_dataset_card(df_multindex_for_agg, subset, metrics_size, add_total=True)
st.dataframe(df_metrics_subset_size)
df_metrics_subset_features = extract_stats_for_dataset_card(df_multindex_for_agg, subset, metrics_features, add_total=False)
st.dataframe(df_metrics_subset_features)
with analysis_bigos_diagnostic:
dataset_name = "amu-cai/pl-asr-bigos-v2-diagnostic"
dataset_short_name = "BIGOS diagnostic"
dataset_version = "V2"
dataset_configs = get_dataset_config_names(dataset_name,trust_remote_code=True)
# remove "all" subset, which is always the last config type
dataset_configs.pop()
print(dataset_configs)
# read the reports for public and secret datasets
[stats_dict_public, contents_dict_public] = read_reports(dataset_name)
# update the metrics for test split with the secret dataset statistics
#stats_dict_public = add_test_split_stats_from_secret_dataset(stats_dict_public, stats_dict_secret)
df_multindex_for_agg = dict_to_multindex_df(stats_dict_public, all_splits=False)
df_multindex_all_splits = dict_to_multindex_df(stats_dict_public, all_splits=True)
# extract metrics from dictionary and convert to various dataframes for easier analysis and visualization
#st.header("Summary statistics")
st.header("Dataset level metrics")
df_sum_stats_agg = extract_stats_to_agg(df_multindex_for_agg, metrics_size)
# split dataframe into separate dataframes for easier analysis and visualization
st.subheader("Audio content size")
df_sum_stats_audio = df_sum_stats_agg[metrics_size_audio]
st.dataframe(df_sum_stats_audio)
st.subheader("Text content size")
df_sum_stats_text = df_sum_stats_agg[metrics_size_text]
st.dataframe(df_sum_stats_text)
df_sum_stats_all_splits = extract_stats_all_splits(df_multindex_all_splits, metrics_features)
st.subheader("Utterances, vocabulary and alphabet space")
df_sum_stats_feats_text = df_sum_stats_all_splits[metrics_features_text_uniq]
st.dataframe(df_sum_stats_feats_text)
st.subheader("Speech rates")
df_sum_stats_feats_speech_rate= df_sum_stats_all_splits[metrics_features_speech_rate]
st.dataframe(df_sum_stats_feats_speech_rate)
st.subheader("Average utterance lengths and audio duration")
df_sum_stats_feats_durations = df_sum_stats_all_splits[metrics_features_duration]
st.dataframe(df_sum_stats_feats_durations)
st.subheader("Metadata coverage")
df_sum_stats_feats_meta = df_sum_stats_all_splits[metrics_features_meta]
st.dataframe(df_sum_stats_feats_meta)
st.header("BIGOS subsets (source datasets) cards")
for subset in dataset_configs:
st.subheader("Dataset card for: {}".format(subset))
df_metrics_subset_size = extract_stats_for_dataset_card(df_multindex_for_agg, subset, metrics_size, add_total=True)
st.dataframe(df_metrics_subset_size)
df_metrics_subset_features = extract_stats_for_dataset_card(df_multindex_for_agg, subset, metrics_features, add_total=False)
st.dataframe(df_metrics_subset_features)
#########################################PELCRA################################################
with analysis_bigos_pelcra:
dataset_name = "pelcra/pl-asr-pelcra-for-bigos"
dataset_short_name = "PELCRA"
# local version with granted gated access
#dataset_configs = get_dataset_config_names(dataset_name,trust_remote_code=True)
# remove "all" subset, which is always the last config type
#dataset_configs.pop()
# remote version with hardcoded access
dataset_configs = ['ul-diabiz_poleval-22', 'ul-spokes_mix_emo-18', 'ul-spokes_mix_luz-18', 'ul-spokes_mix_parl-18', 'ul-spokes_biz_bio-23', 'ul-spokes_biz_int-23', 'ul-spokes_biz_luz-23', 'ul-spokes_biz_pod-23', 'ul-spokes_biz_pres-23', 'ul-spokes_biz_vc-23', 'ul-spokes_biz_vc2-23', 'ul-spokes_biz_wyw-23']
print(dataset_configs)
# read the reports for public and secret datasets
[stats_dict_public, contents_dict_public] = read_reports(dataset_name)
# update the metrics for test split with the secret dataset statistics
#stats_dict_public = add_test_split_stats_from_secret_dataset(stats_dict_public, stats_dict_secret)
df_multindex_for_agg = dict_to_multindex_df(stats_dict_public, all_splits=False)
df_multindex_all_splits = dict_to_multindex_df(stats_dict_public, all_splits=True)
# extract metrics from dictionary and convert to various dataframes for easier analysis and visualization
#st.header("Summary statistics")
st.header("Dataset level metrics")
df_sum_stats_agg = extract_stats_to_agg(df_multindex_for_agg, metrics_size)
# split dataframe into separate dataframes for easier analysis and visualization
st.subheader("Audio content size")
df_sum_stats_audio = df_sum_stats_agg[metrics_size_audio]
st.dataframe(df_sum_stats_audio)
st.subheader("Text content size")
df_sum_stats_text = df_sum_stats_agg[metrics_size_text]
st.dataframe(df_sum_stats_text)
df_sum_stats_all_splits = extract_stats_all_splits(df_multindex_all_splits, metrics_features)
st.subheader("Utterances, vocabulary and alphabet space")
df_sum_stats_feats_text = df_sum_stats_all_splits[metrics_features_text_uniq]
st.dataframe(df_sum_stats_feats_text)
st.subheader("Speech rates")
df_sum_stats_feats_speech_rate= df_sum_stats_all_splits[metrics_features_speech_rate]
st.dataframe(df_sum_stats_feats_speech_rate)
st.subheader("Average utterance lengths and audio duration")
df_sum_stats_feats_durations = df_sum_stats_all_splits[metrics_features_duration]
st.dataframe(df_sum_stats_feats_durations)
st.subheader("Metadata coverage")
df_sum_stats_feats_meta = df_sum_stats_all_splits[metrics_features_meta]
st.dataframe(df_sum_stats_feats_meta)
st.header("BIGOS subsets (source datasets) cards")
for subset in dataset_configs:
st.subheader("Dataset card for: {}".format(subset))
df_metrics_subset_size = extract_stats_for_dataset_card(df_multindex_for_agg, subset, metrics_size, add_total=True)
st.dataframe(df_metrics_subset_size)
df_metrics_subset_features = extract_stats_for_dataset_card(df_multindex_for_agg, subset, metrics_features, add_total=False)
st.dataframe(df_metrics_subset_features)
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