Duplicate from trangiabao17032000/final_tts

.gitattributes

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README.md

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+---
+title: Final Tts
+emoji: 📈
+colorFrom: red
+colorTo: red
+sdk: gradio
+sdk_version: 3.40.1
+app_file: app.py
+pinned: false
+duplicated_from: trangiabao17032000/final_tts
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import torch
+import soundfile as sf
+import gradio as gr
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
+from transformers import SpeechT5HifiGan
+from datasets import load_dataset
+from IPython.display import Audio
+import numpy as np
+model_name = "trangiabao17032000/final_tts"
+
+#processor
+processor = SpeechT5Processor.from_pretrained(model_name)
+tokenizer = processor.tokenizer
+
+#model
+model = SpeechT5ForTextToSpeech.from_pretrained(model_name)
+model.resize_token_embeddings(len(tokenizer))
+model.eval()
+
+#vocoder
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+vocoder.eval()
+
+#speaker embedding
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+
+#cleaner text
+def convert_string_to_numbers(input_str):
+    try:
+        # Replace comma with period and attempt to convert the string to a float
+        num = float(input_str.replace(',', '.'))
+        if num.is_integer():
+            return int(num)
+        return num
+    except ValueError:
+        # If it's not a valid float, check if it's an integer or a negative integer
+        if input_str.replace('.', '', 1).isdigit():  # Remove one dot for checking integers
+            return int(input_str.replace(',', ''))
+        elif input_str[0] == '-' and input_str[1:].replace('.', '', 1).isdigit():
+            return int(input_str.replace(',', ''))
+        else:
+            raise ValueError("Invalid input: couldn't convert to a number")
+
+def number_to_vietnamese_words(number):
+    ones = ['', 'một', 'hai', 'ba', 'bốn', 'năm', 'sáu', 'bảy', 'tám', 'chín']
+    tens = ['', 'mười', 'hai mươi', 'ba mươi', 'bốn mươi', 'năm mươi', 'sáu mươi', 'bảy mươi', 'tám mươi', 'chín mươi']
+    hundreds = ['', 'một trăm', 'Hai trăm', 'ba trăm', 'bốn trăm', 'năm trăm', 'sáu trăm', 'bảy trăm', 'tám trăm', 'chín trăm']
+    thousands = [''] + ['nghìn', 'triệu', 'tỷ']
+
+    def words(n):
+
+        if n < 10:
+            return ones[n]
+        elif n < 20:
+            return tens[n//10]  + " " + words(n % 10)
+        elif n < 100:
+            return tens[n // 10] + ('' if n % 10 == 0 else ' ' + ones[n % 10])
+        else:
+            return hundreds[n // 100] + ('' if n % 100 == 0 else (' lẻ ' if n % 100 < 10 else ' ') + words(n % 100))
+
+    if number == 0:
+        return 'không'
+
+    integer_part = int(number)
+    decimal_part = round((number - integer_part) * 100)  # Round the decimal part to 2 decimal places
+
+    result = []
+    i = 0
+    while integer_part > 0:
+        if integer_part % 1000 != 0:
+            result.append(words(integer_part % 1000) + (' ' + thousands[i] if i > 0 else ''))
+        integer_part //= 1000
+        i += 1
+
+    result_integer = ' '.join(result[::-1])
+
+    result_decimal = ''
+    if decimal_part > 0:
+        result_decimal = ' phẩy'
+        for digit in str(decimal_part):
+            result_decimal += ' ' + ones[int(digit)]
+
+    return result_integer + result_decimal
+    
+def is_num(string):
+    try:
+        float(string)
+    except ValueError:
+        return False
+    return True
+    
+def normalize(input):
+     input = input.lower()
+     newstr = map(lambda x: number_to_vietnamese_words(convert_string_to_numbers(x)) if is_num(x) else x, input.split(" "))
+     return ' '.join(newstr)
+
+def split_paragraph_into_sentences(paragraph, max_chars = 300):
+    sentences = []
+    words = paragraph.split()
+    current_sentence = words[0]
+
+    for word in words[1:]:
+        if len(current_sentence) + len(word) + 1 <= max_chars:
+            current_sentence += ' ' + word
+        else:
+            sentences.append(current_sentence)
+            current_sentence = word
+
+    if current_sentence:
+        sentences.append(current_sentence)
+
+    return sentences
+    
+# generator speech
+def text_to_speech(paragraph):
+    try:
+        paragraph = normalize(paragraph)
+    except:
+        paragraph = paragraph.lower()
+    list_sentence = split_paragraph_into_sentences(paragraph)
+    final_speech = np.array([])
+    
+    for sentence in list_sentence:
+
+      inputs = processor(text=sentence, return_tensors="pt")
+      speech = model.generate_speech(inputs["input_ids"], speaker_embeddings,vocoder=vocoder)
+      final_speech = np.concatenate((final_speech,  speech.numpy()))
+      
+    sf.write("tts_example.wav", final_speech, samplerate=16000)
+    return "tts_example.wav"
+
+tts_examples = [
+    "xin chào mọi người, đây là sản phẩm thử nghiệm cho tiếng việt.",
+    "Mình sẽ tổ chức sinh nhật vào thứ 6 ngày 7 tháng này",
+]
+
+#gradio interface
+iface = gr.Interface(
+    fn=text_to_speech,
+    inputs=gr.Textbox(),
+    outputs=gr.Audio(),
+    title="Text-to-Speech",
+    examples=tts_examples,
+    description="Give me something to say!",
+)
+
+iface.launch()

requirements.txt

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+torch
+transformers
+soundfile 
+datasets 
+IPython
+sentencepiece