Create app.py

app.py

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+import os
+from typing import Text
+import gradio as gr
+import soundfile as sf
+from transformers import pipeline
+import numpy as np
+import torch
+import re
+from speechbrain.pretrained import EncoderClassifier
+
+
+def create_speaker_embedding(speaker_model, waveform: np.ndarray) -> np.ndarray:
+    with torch.no_grad():
+        speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))
+        speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
+        if device.type != 'cuda':
+            speaker_embeddings = speaker_embeddings.squeeze().numpy()
+        else:
+            speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()
+    speaker_embeddings = torch.tensor(speaker_embeddings, dtype=dtype).unsqueeze(0).to(device)
+    return speaker_embeddings
+
+
+def remove_special_characters_s(text: Text) -> Text:
+    chars_to_remove_regex = '[\…\–\"\“\%\‘\”\�\»\«\„\`\'́]'
+    # remove special characters
+    text = re.sub(chars_to_remove_regex, '', text)
+    text = re.sub("՚", "'", text)
+    text = re.sub("’", "'", text)
+    text = re.sub(r'ы', 'и', text)
+    text = text.lower()
+    return text
+
+
+def cyrillic_to_latin(text: Text) -> Text:
+    replacements = [
+        ('а', 'a'),
+        ('б', 'b'),
+        ('в', 'v'),
+        ('г', 'h'),
+        ('д', 'd'),
+        ('е', 'e'),
+        ('ж', 'zh'),
+        ('з', 'z'),
+        ('и', 'y'),
+        ('й', 'j'),
+        ('к', 'k'),
+        ('л', 'l'),
+        ('м', 'm'),
+        ('н', 'n'),
+        ('о', 'o'),
+        ('п', 'p'),
+        ('р', 'r'),
+        ('с', 's'),
+        ('т', 't'),
+        ('у', 'u'),
+        ('ф', 'f'),
+        ('х', 'h'),
+        ('ц', 'ts'),
+        ('ч', 'ch'),
+        ('ш', 'sh'),
+        ('щ', 'sch'),
+        ('ь', "'"),
+        ('ю', 'ju'),
+        ('я', 'ja'),
+        ('є', 'je'),
+        ('і', 'i'),
+        ('ї', 'ji'),
+        ('ґ', 'g')
+    ]
+
+    for src, dst in replacements:
+        text = text.replace(src, dst)
+    return text
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
+
+spk_model_name = "speechbrain/spkrec-xvect-voxceleb"
+
+speaker_model = EncoderClassifier.from_hparams(
+            source=spk_model_name, 
+            run_opts={"device": device}, 
+            savedir=os.path.join("/tmp", spk_model_name)
+            )
+
+waveform, samplerate = sf.read("speaker.wav")
+
+speaker_embeddings = create_speaker_embedding(speaker_model, waveform)
+
+transcriber = pipeline("text-to-speech", model="Oysiyl/speecht5_tts_common_voice_uk")
+
+def transcribe(text: Text) -> tuple((int, np.ndarray)):
+    text = remove_special_characters_s(text)
+    text = cyrillic_to_latin(text)
+    out = transcriber(text, forward_params={"speaker_embeddings": speaker_embeddings})
+    audio, sr = out["audio"], out["sampling_rate"]
+    return sr, audio
+
+
+demo = gr.Interface(
+    transcribe,
+    gr.Textbox(),
+    outputs="audio",
+    title="Text to Speech for Ukrainian language demo",
+    description="Click on the example below or type text!",
+    examples=[["Держава-агресор Росія закуповує комунікаційне обладнання, зокрема супутникові інтернет-термінали Starlink, для використання у війні в арабських країнах"], 
+              ["Доброго вечора, ми з України!"]],
+    cache_examples=True
+)
+
+demo.launch()