openbank-uz/youtube_transcriptions

Dataset Description

A speech dataset of Uzbek language audio clips sourced from YouTube videos. Audio segments were extracted, separated by speaker using vocal isolation, and transcribed using Google's Gemini 2.0 Flash model. Speaker identities were clustered using ECAPA-TDNN embeddings.

Use Cases

• Automatic Speech Recognition (ASR) for Uzbek
• Text-to-Speech (TTS) synthesis for Uzbek
• Fine-tuning speech models on Uzbek language data (e.g., Qwen3-TTS)
• Speaker-conditioned TTS training

Code

• Scraping and transcription: Github Repo

Dataset Structure

Column	Type	Description
audio	audio	Audio column (playable on Hugging Face), 16kHz
file	string	Original audio filename
gender	string	Speaker gender (male / female)
transcription	string	Uzbek text transcription (generated by Gemini 2.0 Flash)
speaker_id	string	Clustered speaker identity (e.g., spk_131), 230 unique speakers
parsed_time	string	Timestamp when the source video was processed
video_title	string	Title of the source YouTube video
youtube_video_duration	string	Duration of the source video
audio_format	string	Audio encoding format (e.g., opus32)
speaker_id_in_video	string	Original diarization speaker label within the video (e.g., 01)
speaker_audio_index	int	Index of this audio segment for the speaker within the video
prompt_tokens_used	int64	Number of prompt tokens consumed during transcription
total_tokens_used	int64	Total tokens consumed during transcription

Example

{
    "file": "2025-01-10_20-08-32_umrimiz_asli_qisqa_emas_..._vocalsSPEAKER_01_0.mp3",
    "gender": "male",
    "transcription": "Haqiqat qoladi, yo'qolmaydi.",
    "speaker_id": "spk_131",
    "parsed_time": "2025-01-10_20-08-32",
    "video_title": "umrimiz asli qisqa emas kopini bekorga sarflab yuboramiz iqtibos podcast 35",
    "youtube_video_duration": "31m43s",
    "audio_format": "opus32",
    "speaker_id_in_video": "01",
    "speaker_audio_index": 0,
    "prompt_tokens_used": 41,
    "total_tokens_used": 72,
    "audio": {"path": "...", "array": [...], "sampling_rate": 16000}
}

Data Collection Pipeline

1. Source: Public Uzbek-language YouTube videos (podcasts, talks, interviews)
2. Audio extraction: Audio tracks extracted from videos and converted to MP3 (Opus 32kbps)
3. Speaker separation: Vocals isolated and split by speaker diarization (speaker segments labeled as SPEAKER_XX)
4. Transcription: Each audio segment transcribed using Gemini 2.0 Flash
5. Gender labeling: Speaker gender annotated per segment
6. Speaker clustering: Global speaker identities assigned across videos (see below)

Speaker Clustering

Speaker identities (speaker_id) were assigned across the full dataset using the following method:

1. Speaker Embeddings were created for each audio
2. Embeddings then were clustered to 230 unique spaekers using Mix of two clustering methods

Embedding Extraction

Speaker embeddings were extracted using SpeechBrain's ECAPA-TDNN model pretrained on VoxCeleb. For code see this notebook: 📒 Google Colab Notebook

Clustering Method

Due to the dataset size (~360K rows), a two-stage clustering approach was used:

1. Optimal cluster count selection: Agglomerative Clustering (cosine distance, average linkage) was run on a 10,000-sample subset, testing cluster counts in range(50, 600, 20). The best silhouette score determined the optimal number of clusters.

2. Full dataset clustering: MiniBatchKMeans (batch size 4096) was applied to all ~360K samples using the optimal cluster count from step 1.

Result: 230 unique speakers identified.

# Step 1: Find optimal n on subset
X_sub = X[:10000]
for n in range(50, 600, 20):
    clustering = AgglomerativeClustering(
        n_clusters=n, metric="cosine", linkage="average"
    )
    labels = clustering.fit_predict(X_sub)
    score = silhouette_score(X_sub, labels, metric="cosine")

# Step 2: Cluster full dataset with best n
clustering = MiniBatchKMeans(
    n_clusters=best_n, batch_size=4096, random_state=42
)
labels = clustering.fit_predict(X)

Caveats

• Clustering is approximate — some speakers may be split across multiple IDs or merged into one, especially for speakers with similar vocal characteristics.
• The original in-video diarization labels (speaker_id_in_video) are preserved for cross-referencing.
• ECAPA-TDNN was not fine-tuned on Uzbek speech, which may affect clustering quality.

Limitations

• Transcriptions are machine-generated (Gemini 2.0 Flash) and may contain errors, especially for domain-specific vocabulary, names, or dialectal speech.
• Audio quality varies depending on the original YouTube source.
• Gender labels may not be verified for every segment.
• Speaker clustering is unsupervised and approximate — not human-verified.
• The dataset is sourced from publicly available YouTube content. If you are a content creator and wish to have your content removed, please open a discussion.

Citation

If you use this dataset, please cite it as:

@dataset{openbank-uz/youtube_transcriptions,
  title={Uzbek YouTube Speech Dataset},
  year={2025},
  source={YouTube},
  transcription_model={Gemini 2.0 Flash},
  speaker_clustering={ECAPA-TDNN + MiniBatchKMeans}
}