huggingface_2023_12_09

2023-12-09

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需要install transformers 库及拥有cuda 核心 pipeline 是hugging face 的基础工具，端到端的工具

from transformers import pipeline
classifier = pipeline("sensiment-analysis")

tes1 = classifier("the book seems interesting!")
print(tes1)

1	print(pipeline("sensiment-analysis")("the book seems interesting!"))

需要注册一个huggingface账号, then You can create a repository from the CLI (skip if you created a repo from the website)

from transformers import pipeline
from datasets import load_dataset
import soundfile as sf

synthesiser = pipeline("text-to-speech", "microsoft/speecht5_tts")

embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
# You can replace this embedding with your own as well.

speech = synthesiser("Hello, my dog is cooler than you!", forward_params={"speaker_embeddings": speaker_embedding})

sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"])

from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset
import torch
import soundfile as sf
from datasets import load_dataset

processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

inputs = processor(text="Hello, my dog is cute.", return_tensors="pt")

# load xvector containing speaker's voice characteristics from a dataset
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)

speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)

sf.write("speech.wav", speech.numpy(), samplerate=16000)

本文作者： hongbo
本文链接： https://hbyecoding.github.io/2023/12/09/huggingface-2023-12-09/
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