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import gradio as gr
import librosa
import numpy as np
import torch
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset, Audio
dataset = load_dataset(
"divakaivan/glaswegian_audio"
)
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))['train']
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("divakaivan/glaswegian_tts")
tokenizer = processor.tokenizer
def extract_all_chars(batch):
all_text = " ".join(batch["transcription"])
vocab = list(set(all_text))
return {"vocab": [vocab], "all_text": [all_text]}
vocabs = dataset.map(
extract_all_chars,
batched=True,
batch_size=-1,
keep_in_memory=True,
remove_columns=dataset.column_names,
)
dataset_vocab = set(vocabs["vocab"][0])
tokenizer_vocab = {k for k,_ in tokenizer.get_vocab().items()}
import os
import torch
from speechbrain.inference.speaker import EncoderClassifier
spk_model_name = "speechbrain/spkrec-xvect-voxceleb"
device = "cuda" if torch.cuda.is_available() else "cpu"
speaker_model = EncoderClassifier.from_hparams(
source=spk_model_name,
run_opts={"device": device},
savedir=os.path.join("/tmp", spk_model_name),
)
def create_speaker_embedding(waveform):
with torch.no_grad():
speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))
speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()
return speaker_embeddings
def prepare_dataset(example):
# load the audio data; if necessary, this resamples the audio to 16kHz
audio = example["audio"]
# feature extraction and tokenization
example = processor(
text=example["transcription"],
audio_target=audio["array"],
sampling_rate=audio["sampling_rate"],
return_attention_mask=False,
)
# strip off the batch dimension
example["labels"] = example["labels"][0]
# use SpeechBrain to obtain x-vector
example["speaker_embeddings"] = create_speaker_embedding(audio["array"])
return example
processed_example = prepare_dataset(dataset[0])
from transformers import SpeechT5HifiGan
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
spectrogram = torch.tensor(processed_example["labels"])
with torch.no_grad():
speech = vocoder(spectrogram)
dataset = dataset.map(
prepare_dataset, remove_columns=dataset.column_names,
)
dataset = dataset.train_test_split(test_size=0.1)
def predict(text, speaker):
if len(text.strip()) == 0:
return (16000, np.zeros(0).astype(np.int16))
inputs = processor(text=text, return_tensors="pt")
# limit input length
# input_ids = inputs["input_ids"]
# input_ids = input_ids[..., :model.config.max_text_positions]
### ### ###
example = dataset['train'][888]
speaker_embeddings = torch.tensor(example["speaker_embeddings"]).unsqueeze(0)
spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)
with torch.no_grad():
speech = vocoder(spectrogram)
speech = (speech.numpy() * 32767).astype(np.int16)
return (16000, speech)
title = "Glaswegian TTS"
article = "Model fine-tuned and gradle demo generated thanks to this notebook: https://colab.research.google.com/drive/1i7I5pzBcU3WDFarDnzweIj4-sVVoIUFJ#scrollTo=wm7B3zxrumfF"
gr.Interface(
fn=predict,
inputs=[
gr.Text(label="Input Text"),
],
outputs=[
gr.Audio(label="Generated Speech", type="numpy"),
],
title=title,
article=article,
).launch()
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