Dataset Preview - Initial commit

.gitignore

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+# python venvs
+.venv
+
+# env files
+.env
+
+# local streamlit state
+.streamlit/
+
+# pycache
+__pycache__/
+*.py[cod]
+

.python-version

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+3.11.9

app.py

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+import json
+import math
+import os
+import random
+
+import pandas as pd
+import streamlit as st
+from huggingface_hub import HfApi
+
+st.set_page_config(page_title="Knesset Plenums Dataset Preview", layout="wide")
+
+fallback_dataset_repo_owner = os.environ.get("REPO_OWNER", "ivrit.ai")
+dataset_repo_owner = os.environ.get("SPACE_AUTHOR_NAME", fallback_dataset_repo_owner)
+dataset_repo_name = os.environ.get("DATASET_REPO_NAME", "knesset-plenums")
+repo_id = f"{dataset_repo_owner}/{dataset_repo_name}"
+
+hf_api = HfApi(token=st.secrets["HF_TOKEN"])
+
+manifest_file = hf_api.hf_hub_download(repo_id, "manifest.csv", repo_type="dataset")
+
+manifest_df = pd.read_csv(manifest_file)
+
+# Filter samples with duration less than 7200 seconds (2 hours)
+filtered_samples = manifest_df[manifest_df["duration"] < 7200].copy()
+
+# Convert duration from seconds to hours for display
+filtered_samples["duration_hours"] = filtered_samples["duration"] / 3600
+
+# Create display options for the dropdown
+sample_options = {}
+for _, row in filtered_samples.iterrows():
+    plenum_id = str(row["plenum_id"])
+    plenum_date = row["plenum_date"]
+    hours = round(row["duration_hours"], 1)
+    display_text = f"{plenum_date} - ({hours} hours)"
+    sample_options[display_text] = plenum_id
+
+# Default to sample_id 81733 if available, otherwise use the first sample
+default_sample_id = "81733"
+default_option = next(
+    (k for k, v in sample_options.items() if v == default_sample_id),
+    next(iter(sample_options.keys())) if sample_options else None,
+)
+
+# Create the dropdown for sample selection
+selected_option = st.sidebar.selectbox(
+    "Select a plenum sample:",
+    options=list(sample_options.keys()),
+    index=list(sample_options.keys()).index(default_option) if default_option else 0,
+)
+
+# Get the selected plenum ID
+sample_plenum_id = sample_options[selected_option]
+sample_audio_file_repo_path = f"{sample_plenum_id}/audio.m4a"
+sample_metadata_file_repo_path = f"{sample_plenum_id}/metadata.json"
+sample_aligned_file_repo_path = f"{sample_plenum_id}/transcript.aligned.json"
+sample_raw_text_repo_path = f"{sample_plenum_id}/raw.transcript.txt"
+
+
+# Display the title with the selected Plenum ID
+st.title(f"Knesset Plenum ID: {sample_plenum_id}")
+
+
+# Cache the sample data loading to only reload when the sample changes
+@st.cache_data
+def load_sample_data(repo_id, plenum_id):
+    """Load sample data files for a given plenum ID"""
+    audio_path = f"{plenum_id}/audio.m4a"
+    metadata_path = f"{plenum_id}/metadata.json"
+    transcript_path = f"{plenum_id}/transcript.aligned.json"
+
+    audio_file = hf_api.hf_hub_download(repo_id, audio_path, repo_type="dataset")
+    metadata_file = hf_api.hf_hub_download(repo_id, metadata_path, repo_type="dataset")
+    transcript_file = hf_api.hf_hub_download(
+        repo_id, transcript_path, repo_type="dataset"
+    )
+    raw_transcript_text_file = hf_api.hf_hub_download(
+        repo_id, sample_raw_text_repo_path, repo_type="dataset"
+    )
+
+    return audio_file, metadata_file, transcript_file, raw_transcript_text_file
+
+
+# Load the sample data for the selected plenum
+(
+    sample_audio_file,
+    sample_metadata_file,
+    sample_transcript_aligned_file,
+    sample_raw_transcript_text_file,
+) = load_sample_data(repo_id, sample_plenum_id)
+
+# Parses the metadata file of this sample - to get the list of all segments.
+with open(sample_metadata_file, "r") as f:
+    sample_metadata = json.load(f)
+
+# each segment is a dict with the structure:
+# {
+#   "start": 3527.26,
+#   "end": 3531.53,
+#   "probability": 0.9309
+# },
+segments_quality_scores = sample_metadata["per_segment_quality_scores"]
+segments_quality_scores_df = pd.DataFrame(segments_quality_scores)
+segments_quality_scores_df["segment_id"] = segments_quality_scores_df.index
+
+with open(sample_transcript_aligned_file, "r") as f:
+    sample_transcript_aligned = json.load(f)
+transcript_segments = sample_transcript_aligned["segments"]
+
+with open(sample_raw_transcript_text_file, "r") as f:
+    sample_raw_text = f.read()
+
+col_main, col_aux = st.columns([2, 3])
+
+event = col_main.dataframe(
+    segments_quality_scores_df,
+    on_select="rerun",
+    hide_index=True,
+    selection_mode=["single-row"],
+    column_config={
+        "probability": st.column_config.ProgressColumn(
+            label="Quality Score",
+            width="medium",
+            format="percent",
+            min_value=0,
+            max_value=1,
+        )
+    },
+)
+
+
+# Initialize session state for selection if it doesn't exist
+if "default_selection" not in st.session_state:
+    st.session_state.default_selection = random.randint(
+        0, min(49, len(segments_quality_scores_df) - 1)
+    )
+
+# If a selection exists, get the start and end times of the selected segment
+if event and event.selection and event.selection["rows"]:
+    row_idx = event.selection["rows"][0]
+else:
+    # Use the default random selection if no row is selected
+    row_idx = st.session_state.default_selection
+
+df_row = segments_quality_scores_df.iloc[row_idx]
+segment_id = int(df_row["segment_id"])
+selected_segment = segments_quality_scores[segment_id]
+start_time = selected_segment["start"]
+end_time = selected_segment["end"]
+
+with col_main:
+    st.write(f"Selected segment: {selected_segment}")
+    start_at = selected_segment["start"]
+    end_at = selected_segment["end"]
+
+    st.audio(
+        sample_audio_file,
+        start_time=math.floor(start_at),
+        end_time=math.ceil(end_at),
+        autoplay=True,
+    )
+    transcript_segment = transcript_segments[segment_id]
+    st.caption(f'<div dir="rtl">{transcript_segment["text"]}</div>', unsafe_allow_html=True)
+    st.divider()
+    st.caption(
+        f"Note: The audio will start at {math.floor(start_at)} seconds and end at {math.ceil(end_at)} seconds (rounded up/down) since this is the resolution of the player, actual segments are more accurate."
+    )
+
+
+with col_aux:
+    # Create a chart of Quality vs start_time
+    st.subheader("Segment Quality Over Time")
+    
+    # Prepare data for the chart
+    chart_data = segments_quality_scores_df.copy()
+    chart_data = chart_data.sort_values(by="start")
+    
+    # Add a scatter plot to highlight the selected segment
+    import altair as alt
+    import pandas as pd
+    
+    # Create a base chart with all points
+    base_chart = alt.Chart(chart_data).mark_circle(size=20).encode(
+        x=alt.X('start:Q', title='Start Time (seconds)'),
+        y=alt.Y('probability:Q', title='Quality Score', scale=alt.Scale(domain=[0, 1])),
+        tooltip=['start', 'end', 'probability']
+    )
+    
+    # Create a highlight for the selected segment
+    selected_point = pd.DataFrame([{
+        'start': selected_segment['start'],
+        'probability': selected_segment['probability']
+    }])
+    
+    highlight = alt.Chart(selected_point).mark_circle(size=120, color='red').encode(
+        x='start:Q',
+        y='probability:Q'
+    )
+    
+    # Combine the charts
+    combined_chart = base_chart + highlight
+    
+    # Display the chart
+    st.altair_chart(combined_chart, use_container_width=True)
+    
+    with st.expander("Raw Transcript Text", expanded=False):
+        st.text_area(
+            "Raw Transcript Text",
+            value=sample_raw_text,
+            height=300,
+            label_visibility="collapsed",
+            disabled=True,
+        )
+
+    with st.expander("Sample Metadata", expanded=False):
+        st.json(
+            sample_metadata
+        )

pyproject.toml

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+[project]
+name = "knesset-plenums-preview"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11.9"
+dependencies = [
+    "datasets>=3.5.0",
+    "huggingface-hub>=0.30.2",
+    "pandas>=2.2.3",
+    "python-dotenv>=1.1.0",
+    "streamlit==1.44.1",
+]

requirements.txt

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+streamlit==1.44.1
+datasets
+huggingface_hub
+pandas