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Update app.py
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app.py
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@@ -1,6 +1,5 @@
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import gradio as gr
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from transformers import pipeline,
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import torch
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import numpy as np
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# Load the pipeline for speech recognition and translation
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tokenizer="Baghdad99/saad-speech-recognition-hausa-audio-to-text"
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)
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translator = pipeline("text2text-generation", model="Baghdad99/saad-hausa-text-to-english-text")
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# Load the VITS model for text-to-speech synthesis
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tts_model = VitsModel.from_pretrained("Baghdad99/english_voice_tts")
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tts_tokenizer = AutoTokenizer.from_pretrained("Baghdad99/english_voice_tts")
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# Define the function to translate speech
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def translate_speech(audio):
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print("The translated text does not contain 'generated_token_ids'")
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return
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# Use the
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with torch.no_grad():
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synthesised_speech = tts_model(**tts_inputs).waveform
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print(f"Synthesised speech: {synthesised_speech}") # Print the synthesised speech to see what it contains
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#
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return 16000, synthesised_speech
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import gradio as gr
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from transformers import pipeline, AutoTokenizer
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import numpy as np
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# Load the pipeline for speech recognition and translation
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tokenizer="Baghdad99/saad-speech-recognition-hausa-audio-to-text"
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)
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translator = pipeline("text2text-generation", model="Baghdad99/saad-hausa-text-to-english-text")
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tts = pipeline("text-to-speech", model="Baghdad99/english_voice_tts")
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# Define the function to translate speech
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def translate_speech(audio):
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print("The translated text does not contain 'generated_token_ids'")
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return
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# Use the text-to-speech pipeline to synthesize the translated text
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synthesised_speech = tts(translated_text_str)
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print(f"Synthesised speech: {synthesised_speech}") # Print the synthesised speech to see what it contains
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# Check if the synthesised speech contains 'audio'
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if 'audio' in synthesised_speech:
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synthesised_speech_data = synthesised_speech['audio']
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else:
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print("The synthesised speech does not contain 'audio'")
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return
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# Scale the audio data to the range of int16 format
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synthesised_speech = (synthesised_speech_data * 32767).astype(np.int16)
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return 16000, synthesised_speech
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