Gun Detection Dataset
This is a gun detection dataset with 51K annotated gun images for gun detection and other 51K cropped gun chip images for gun classification collected from a few different sources.
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
import gTTS import os def text_to_speech_kurdish(text, output_file="output.mp3"): # گۆڕینی نووسین بۆ دەنگ بە زمانی کوردی (هەڵبژاردنی زمانی "ku" بۆ کوردی) tts = gTTS(text=text, lang='ku', slow=False) tts.save(output_file) os.system(f"start {output_file}") # کردنەوەی فایلە دەنگییەکە (لە Windows) # نموونە: text_to_speech_kurdish("سڵاو، ئەمە دەنگی منە بە زمانی کوردی.")
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
This is a gun detection dataset with 51K annotated gun images for gun detection and other 51K cropped gun chip images for gun classification collected from a few different sources.
OpenSLR is a repository of open speech and language resources, including large-scale transcribed audio corpora and related software. It serves as a central platform for researchers and practitioners to access and share datasets used in speech recognition (ASR), text-to-speech (TTS), and linguistic research. The OpenSLR collection includes over 30 diverse datasets spanning more than 25 languages, such as Javanese, Nepali, Malayalam, Yoruba, and various English and Spanish dialects. These datasets are contributed by institutions including Google, North West University, and others. Most contain audio recordings along with transcriptions, covering both crowd-sourced and professionally recorded material. Many of the datasets are high-quality multi-speaker corpora intended for use in building ASR and TTS models, particularly for under-resourced languages. Use cases include multilingual speech recognition, dialect modeling, language technology research, and building open-source voice applications. The OpenSLR site also acts as a mirror for widely-used tools and models to ensure continued availability. Website: OpenSLR website Source: openslr/openslr
We introduce ArVoice, a multi-speaker Modern Standard Arabic (MSA) speech corpus with diacritized transcriptions, intended for multi-speaker speech synthesis, and can be useful for other tasks such as speech-based diacritic restoration, voice conversion, and deepfake detection. ArVoice comprises: (1) a new professionally recorded set from six voice talents with diverse demographics, (2) a modified subset of the Arabic Speech Corpus; and (3) high-quality synthetic speech from two commercial systems. The complete corpus consists of a total of 83.52 hours of speech across 11 voices; around 10 hours consist of human voices from 7 speakers. We train three open-source TTS and two voice conversion systems to illustrate the use cases of the dataset. The corpus is available for research use.
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