在我們的全球化世界中，跨越語言界線的溝通比以往任何時候都更加重要。在這篇文章中，我們將探討如何實施這項技術，讓溝通變得更包容、方便每個人。
程式碼可以在這裡取得
在我的 github 上
首先要做的是安裝相依性
blinker==1.8.2 cachetools==5.5.0 certifi==2024.8.30 chardet==3.0.4 charset-normalizer==3.4.0 click==8.1.7 colorama==0.4.6 Flask==3.0.3 google-api-core==2.22.0 google-auth==2.36.0 google-cloud-texttospeech==2.21.0 googleapis-common-protos==1.65.0 googletrans==4.0.0rc1 grpcio==1.67.1 grpcio-status==1.67.1 gTTS==2.5.3 h11==0.9.0 h2==3.2.0 hpack==3.0.0 hstspreload==2024.11.1 httpcore==0.9.1 httpx==0.13.3 hyperframe==5.2.0 idna==2.10 itsdangerous==2.2.0 Jinja2==3.1.4 Levenshtein==0.26.1 MarkupSafe==3.0.2 playsound==1.2.2 prompt_toolkit==3.0.48 proto-plus==1.25.0 protobuf==5.28.3 pyasn1==0.6.1 pyasn1_modules==0.4.1 PyAudio==0.2.14 python-Levenshtein==0.26.1 RapidFuzz==3.10.1 requests==2.32.3 rfc3986==1.5.0 rsa==4.9 sniffio==1.3.1 SpeechRecognition==3.11.0 typing_extensions==4.12.2 urllib3==2.2.3 wcwidth==0.2.13 Werkzeug==3.1.2 wit==6.0.1
音訊到文字轉換
from gtts import gTTS
import playsound
import os
def speak_translation(text, lang):
tts = gTTS(text=text, lang=lang)
filename = "translation.mp3"
tts.save(filename)
playsound.playsound(filename)
os.remove(filename)
Google雲端文字語音
from google.cloud import texttospeech
def synthesize_speech(text, language_code="wo-WO", voice_name="wo-WO-Standard-A", output_file="output.mp3"):
client = texttospeech.TextToSpeechClient()
input_text = texttospeech.SynthesisInput(text=text)
# Configurez la voix pour le Wolof
voice = texttospeech.VoiceSelectionParams(
language_code=language_code,
name=voice_name,
ssml_gender=texttospeech.SsmlVoiceGender.NEUTRAL,
)
# Paramètres audio
audio_config = texttospeech.AudioConfig(
audio_encoding=texttospeech.AudioEncoding.MP3
)
# Synthèse vocale
response = client.synthesize_speech(
input=input_text, voice=voice, audio_config=audio_config
)
# Sauvegarder le fichier audio
with open(output_file, "wb") as out:
out.write(response.audio_content)
print(f"Audio content written to file {output_file}")
# Utilisez cette fonction avec votre texte
synthesize_speech("Bonjour, je teste la traduction en Wolof.", "wo-WO")
翻譯
from googletrans import Translator
def translate_text(text, target_lang):
try:
translator = Translator()
translation = translator.translate(text, dest=target_lang)
print(f"Traduction : {translation.text}")
return translation.text
except Exception as e:
print(f"Erreur lors de la traduction : {e}")
return "Traduction non disponible"
語音偵測
import speech_recognition as sr
def record_audio():
recognizer = sr.Recognizer()
with sr.Microphone() as source:
print("Parlez maintenant...")
audio = recognizer.listen(source)
try:
text = recognizer.recognize_google(audio, language="fr-FR")
print(f"Vous avez dit : {text}")
return text
except sr.UnknownValueError:
print("Désolé, je n'ai pas compris.")
except sr.RequestError as e:
print(f"Erreur de service : {e}")
Witai 參數：
您必須前往 Meta API (Facebook) 來建立您的代幣
import requests
WIT_AI_TOKEN = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
def send_to_wit(text):
headers = {'Authorization': f'Bearer {WIT_AI_TOKEN}'}
response = requests.get(f'https://api.wit.ai/message?v=20230414&q={text}', headers=headers)
return response.json()
主檔
from flask import Flask, request, jsonify
from convertion_audio_to_text import speak_translation
from translation import translate_text
from voice_detection import record_audio
from witai_params import send_to_wit
import Levenshtein
app = Flask(__name__)
# Langues disponibles
AVAILABLE_LANGUAGES = {
"sw": "Swahili",
"wo": "Wolof",
"fon": "Fon",
"en": "Anglais",
"fr": "Français"
}
def calculate_score(reference_text, user_text):
similarity = Levenshtein.ratio(reference_text.lower(), user_text.lower()) * 100
return round(similarity, 2)
@app.route('/available_languages', methods=['GET'])
def available_languages():
"""Retourne les langues disponibles pour la traduction."""
return jsonify(AVAILABLE_LANGUAGES)
@app.route('/process_audio', methods=['POST'])
def process_audio():
"""Traite l'audio, traduit le texte et évalue la prononciation."""
try:
# Étape 1 : Récupérer la langue cible depuis la requête
target_lang = request.json.get('target_lang')
if not target_lang:
return jsonify({"error": "Paramètre 'target_lang' manquant"}), 400
if target_lang not in AVAILABLE_LANGUAGES:
return jsonify({
"error": f"Langue cible '{target_lang}' non supportée.",
"available_languages": AVAILABLE_LANGUAGES # Retourner la liste des langues disponibles
}), 400
# Étape 2 : Traduire le texte initial
text = record_audio()
if not text:
return jsonify({"error": "No audio detected or transcription failed"}), 400
wit_response = send_to_wit(text)
print("Wit.ai Response:", wit_response)
translation = translate_text(text, target_lang)
speak_translation(translation, lang=target_lang)
# Étape 3 : Boucle de répétition pour évaluer la prononciation
score = 0
while score < 80:
repeat_text = record_audio()
if not repeat_text:
return jsonify({"error": "No repeated audio detected"}), 400
score = calculate_score(translation, repeat_text)
if score >= 80:
message = "Bravo! Félicitations, vous êtes un génie!"
return jsonify({
"original_text": text,
"wit_response": wit_response,
"translated_text": translation,
"repeated_text": repeat_text,
"score": score,
"message": message
}), 200
elif score < 45:
message = "Votre score est faible, améliorez-vous en vous entraînant."
else:
message = "Pas mal! Vous pouvez encore améliorer."
return jsonify({
"translated_text": translation,
"repeated_text": repeat_text,
"score": score,
"message": message,
"retry": True
})
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == '__main__':
app.run(debug=True)
"""
tu peux tester avec ce code dans le navigateur, tu decommente, puis tu le met la ou il faut
@app.route('/process_audio', methods=['GET', 'POST'])
def process_audio():
if request.method == 'GET':
return jsonify({"message": "Utilisez une requête POST pour traiter l'audio."})
# Continue avec la logique POST
try:
text = record_audio()
if not text:
return jsonify({"error": "No audio detected or transcription failed"}), 400
wit_response = send_to_wit(text)
print("Wit.ai Response:", wit_response)
target_lang = request.json.get('target_lang', 'sw')
translation = translate_text(text, target_lang)
speak_translation(translation, lang=target_lang)
return jsonify({
"original_text": text,
"wit_response": wit_response,
"translated_text": translation
}), 200
except Exception as e:
return jsonify({"error": str(e)}), 500
"""
如今，設計機器人來解決我們日常生活中的複雜問題變得越來越容易。不過，這並不排除自學語言的重要性。使用 BotAI 等技術進行即時語音翻譯應該主要是為了豐富我們在複雜環境中的互動。透過將這些工具與個人語言學習結合，我們可以促進更有效的溝通，同時促進個人語言財富。
程式碼可以在這裡取得
在我的 github 上
以上是如何使用 witai 創建語音翻譯機器人的詳細內容。更多資訊請關注PHP中文網其他相關文章！
