Datasets:
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ملخبن اسمي اخمد وانا اتخدث بلغة العربية |
تقنيت ذكائل استنعيية تططور بشكل متسارعا في عصرنا الحالية |
Guten Tag, mein Name ist Klaus und ich spreche auf Deutsch. |
Die Spracherkennungstechnologie hat sich in den letzten Jahren stark verbessert. |
Künstliche Intelligenz verändert die Art und Weise, wie wir kommunizieren. |
The quick brown fox jumps over the lazy dog. |
Speech recognition technology has advanced significantly in recent years. |
Hello, my name is Alex and I am testing the voice data curator. |
Artificial intelligence is transforming the way we process language. |
Please speak clearly into the microphone for best results. |
Hola, me llamo Carlos y estoy hablando en español. |
La tecnología de inteligencia artificial avanza rápidamente. |
El reconocimiento de voz es una herramienta muy útil hoy en día. |
Buenos días, ¿cómo estás tu hoy? |
Bonjour, je m'appelle Marie et je parla en français. |
La technologie de reconnaissance vocale a fait de grands progrès. |
Le ciel est bleu et le soleil brie aujourd'hui. |
l'intelligence artificielle transforme notre façon de vivre. |
Namaste, miraname Ananya hai, and me Hindi me bhol Rahin. |
Bharath ikvivith tao se Bharath deesh hai jaha anik baashai booli jathi hai. |
Aug. 25달 17th, 20 natus, 19 natus. |
Kritrim Buddha Dima Taka Upiyog Aajhar Kshetrami Hora Ha hai. |
YA É C |
こんにちは、私の名前はたなかです。日本語でお話します。 |
人口知能は私たちの生活を大きく変えています |
音声に引き切るとはきんねを起きく進ぼしています。 |
Namaskar, mazenau Priya Aayani Mimara Tithbola Taheen. |
Maharashtra is Bharata Thilek Mahatwasi Raja is. |
As Zihaman Kux Henah 130 economic struggle |
</ Kontakiljinhevo Cumits 3-4宛茶y jeu </ Kontakiljinhevo Cumits 3-4宜室 </ Kontakiljinhevo Cumits 4宛茶y jeu </ Kontakiljinhevo Cumits 5宛茶y jeu 🖤 🖐️ � antennaeet 🖐️ 🖐️ 🖐️ 🖐️ 🗣️ 🖐️ 🖐️ 🖐️ �oline 🤤️ 🖐️ � سوف侍 come Remove Coins 🖐️ 🖑️ 🖐️🖐️ 🖐️ 🖐️ 🖐️ 🖐️ 🖐️ 🖐️ |
VoiceDataCurator Multilingual Speech Sample
A small multilingual speech dataset curated using the VoiceDataCurator automated quality control pipeline.
Dataset Description
This dataset contains audio clips (MP3) across 8 languages that passed automated quality checks including SNR analysis, silence ratio detection, clipping detection, and duration validation.
Languages
English, Hindi, Arabic, French, Spanish, German, Japanese, Marathi
Quality Criteria Applied
- Minimum SNR: 10dB
- Maximum silence ratio: 40%
- Maximum clipping ratio: 1%
- Duration: 1–30 seconds
- Language verified via OpenAI Whisper
Dataset Structure
Each sample includes a .mp3 audio file and a .txt transcript file located in the data/ directory.
The dataset_manifest.csv contains per-file quality metrics for all samples (including rejected ones for reference, but only accepted ones are in data/).
How It Was Created
Audio was generated using gTTS and processed through the VoiceDataCurator pipeline. Transcripts were auto-generated using Whisper and should be treated as approximate.
Intended Use
Demonstration dataset for multilingual speech pipeline research and Whisper fine-tuning experiments.
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