122 lines
4.1 KiB
Python
122 lines
4.1 KiB
Python
import whisper
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import os
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import nltk
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import boto3
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import random
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nltk.download('words')
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from nltk.corpus import words
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from helper.constants import *
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def speech_to_text(file_path):
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if os.path.exists(file_path):
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model = whisper.load_model("base")
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result = model.transcribe(file_path, fp16=False, language='English', verbose=False)
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return result["text"]
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else:
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print("File not found:", file_path)
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raise Exception("File " + file_path + " not found.")
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def text_to_speech(text: str, file_name: str):
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# Initialize the Amazon Polly client
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client = boto3.client(
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'polly',
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region_name='eu-west-1',
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aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
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aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY")
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)
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voice = random.choice(ALL_NEURAL_VOICES)['Id']
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# Initialize an empty list to store audio segments
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audio_segments = []
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for part in divide_text(text):
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tts_response = client.synthesize_speech(
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Engine="neural",
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Text=part,
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OutputFormat="mp3",
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VoiceId=voice
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)
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audio_segments.append(tts_response['AudioStream'].read())
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# Add finish message
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audio_segments.append(client.synthesize_speech(
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Engine="neural",
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Text="This audio recording, for the listening exercise, has finished.",
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OutputFormat="mp3",
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VoiceId="Stephen"
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)['AudioStream'].read())
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# Combine the audio segments into a single audio file
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combined_audio = b"".join(audio_segments)
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# Save the combined audio to a single file
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with open(file_name, "wb") as f:
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f.write(combined_audio)
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print("Speech segments saved to " + file_name)
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def conversation_text_to_speech(conversation: list, file_name: str):
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# Initialize the Amazon Polly client
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client = boto3.client(
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'polly',
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region_name='eu-west-1',
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aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
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aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY")
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)
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# Initialize an empty list to store audio segments
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audio_segments = []
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# Iterate through the text segments, convert to audio segments, and store them
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for segment in conversation:
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response = client.synthesize_speech(
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Engine="neural",
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Text=segment["text"],
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OutputFormat="mp3",
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VoiceId=segment["voice"]
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)
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audio_segments.append(response['AudioStream'].read())
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# Add finish message
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audio_segments.append(client.synthesize_speech(
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Engine="neural",
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Text="This audio recording, for the listening exercise, has finished.",
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OutputFormat="mp3",
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VoiceId="Stephen"
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)['AudioStream'].read())
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# Combine the audio segments into a single audio file
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combined_audio = b"".join(audio_segments)
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# Save the combined audio to a single file
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with open(file_name, "wb") as f:
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f.write(combined_audio)
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print("Speech segments saved to " + file_name)
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def has_words(text: str):
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english_words = set(words.words())
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words_in_input = text.split()
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return any(word.lower() in english_words for word in words_in_input)
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def has_x_words(text: str, quantity):
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english_words = set(words.words())
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words_in_input = text.split()
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english_word_count = sum(1 for word in words_in_input if word.lower() in english_words)
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return english_word_count >= quantity
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def divide_text(text, max_length=3000):
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if len(text) <= max_length:
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return [text]
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divisions = []
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current_position = 0
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while current_position < len(text):
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next_position = min(current_position + max_length, len(text))
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next_period_position = text.rfind('.', current_position, next_position)
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if next_period_position != -1 and next_period_position > current_position:
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divisions.append(text[current_position:next_period_position + 1])
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current_position = next_period_position + 1
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else:
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# If no '.' found in the next chunk, split at max_length
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divisions.append(text[current_position:next_position])
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current_position = next_position
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return divisions |