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Browse files- .gitattributes +35 -35
- README.md +13 -13
- app.py +71 -0
- requirements.txt +15 -0
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README.md
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---
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title:
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emoji:
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colorFrom:
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colorTo:
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sdk: gradio
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sdk_version: 5.
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app_file: app.py
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pinned: false
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license:
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Technical Vocabulary
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emoji: 👁
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colorFrom: indigo
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colorTo: purple
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sdk: gradio
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sdk_version: 5.1.0
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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import torch
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import os
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from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
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from datasets import load_dataset, Audio
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import numpy as np
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from speechbrain.inference import EncoderClassifier
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# Load models and processor
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processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
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model = SpeechT5ForTextToSpeech.from_pretrained("tdnathmlenthusiast/speecht5_finetuned_German_dataset")
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
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# Load speaker encoder
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device = "cuda" if torch.cuda.is_available() else "cpu"
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speaker_model = EncoderClassifier.from_hparams(
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source="speechbrain/spkrec-xvect-voxceleb",
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run_opts={"device": device},
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savedir=os.path.join("/tmp", "speechbrain/spkrec-xvect-voxceleb")
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)
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def create_speaker_embedding(waveform):
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with torch.no_grad():
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speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))
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speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
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speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()
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return speaker_embeddings
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# Load a sample from the dataset for speaker embedding
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try:
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dataset = load_dataset(""Thorsten-Voice/TV-44kHz-Full", "TV-2023.09-Hessisch", split="train", trust_remote_code=True)
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dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
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sample = dataset[10]
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speaker_embedding = create_speaker_embedding(sample['audio']['array'])
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except Exception as e:
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print(f"Error loading dataset: {e}")
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# Use a random speaker embedding as fallback
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speaker_embedding = torch.randn(1, 512)
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def text_to_speech(text):
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# Clean up text
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replacements = [
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("0", "zero"),
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("1", "one"),
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("2", "two"),
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("3", "three"),
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("4", "four"),
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("5", "five"),
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("6", "six"),
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("7", "seven"),
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("8", "eight"),
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("9", "nine"),
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("_", " ")
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]
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for src, dst in replacements:
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text = text.replace(src, dst)
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inputs = processor(text=text, return_tensors="pt")
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speech = model.generate_speech(inputs["input_ids"], speaker_embedding, vocoder=vocoder)
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return (16000, speech.numpy())
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iface = gr.Interface(
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fn=text_to_speech,
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inputs="text",
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outputs="audio",
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title="German Text-to-Speech Using T5 by Tirtha Debnath ",
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description="Enter German text to convert to speech"
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)
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iface.launch()
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requirements.txt
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pip
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git+https://github.com/huggingface/transformers.git
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accelerate
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datasets
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soundfile
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speechbrain
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torch
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gradio
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spaces
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python_multipart
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huggingface-hub
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hf-transfer
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protobuf
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click
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pydantic
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