Bark 是 Transformers 支持的一个文本转语音 (Text-To-Speech, TTS) 模型。所有优化仅依赖于 Transformers、Optimum 以及Accelerate 这三个 生态系统库。 本教程还演示了如何对模型及其不同的优化方案进行性能基准测试。 本文对应的 Google Colab 在:colab.research.google.com 本文结构如下: 目录 Bark 模型 简介 不...
wav=tts.tts(text="Hello world!",speaker_wav="my/cloning/audio.wav",language="en")# Text to speech to a file tts.tts_to_file(text="Hello world!",speaker_wav="my/cloning/audio.wav",language="en",file_path="output.wav")tts=TTS("tts_models/de/thorsten/tacotron2-DDC")tts.tts_with...
text_prompt=["Let's try generating speech, with Bark, a text-to-speech model","Wow, batching is so great!","I love Hugging Face, it's so cool."]inputs=processor(text_prompt).to(device)withtorch.inference_mode():# samples are generated all at oncespeech_output=model.generate(**input...
pipeline对于text-to-audio/text-to-speech的默认模型是suno/bark-small,使用pipeline时,如果仅设置task=text-to-audio或task=text-to-speech,不设置模型,则下载并使用默认模型。 import osos.environ["HF_ENDPOINT"] = "https://hf-mirror.com"os.environ["CUDA_VISIBLE_DEVICES"] = "2"import scipyfrom IPyt...
text_prompt ="Let's try generating speech, with Bark, a text-to-speech model" inputs = processor(text_prompt).to(device) 测量延迟和 GPU 内存占用需要使用特定的 CUDA 函数。我们实现了一个工具函数,用于测量模型的推理延迟及 GPU 内存占用。为了确保结果的准确性,每次测量我们会运行nb_loops次求均值:...
在人工智能领域,文本转音频(Text-to-Audio/Text-to-Speech, TTS)技术作为一项重要成果,不仅极大地丰富了人机交互的方式,还广泛应用于教育、娱乐、新闻播报等多个领域。本文将以Hugging Face Transformers库中的Pipeline功能为切入点,深入探讨文本转音频技术的原理、应用场景及实战操作。 Transformers库与Pipeline功能 Huggi...
摘要——卷积增强变换器(Convolution-augmented transformer,Conformer)最近在语音领域的应用中显示出竞争性的结果,如自动语音识别、连续语音分离和声音事件检测。Conformer可以通过多… RainM...发表于语音增强论... NaturalSpeech模型合成语音在CMOS测试中首次达到真人语音水平 微软亚洲研究院打开...
This paper evaluates the quality of Estonian text-tospeech with Transformer-based models using different language-specific data processing steps. Additionally, we conduct a human evaluation to show how well these models can learn the patterns of Estonian pronunciation, given differen...
Transformers have now become the defacto standard for NLP tasks. Originally developed for sequence transduction processes such asspeech recognition, translation, and text to speech, transformers work by using convolutional neural networks together with attention models, making them much more efficient than...
🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on ourmodel hub. At the same time, each python module defining an architecture is fully standalone and can be modifi...