cleanup

app.py

@@ -1,23 +1,17 @@
 import gradio as gr
-import librosa
 import logging
 import numpy as np
 import torch
 
 from transformers import VitsModel, VitsTokenizer, pipeline
-from transformers import WhisperForConditionalGeneration, WhisperProcessor
 
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
-target_language = "fr"
+target_language = "french"
 
 # load speech translation checkpoint
 asr_pipe = pipeline("automatic-speech-recognition", model="bofenghuang/whisper-small-cv11-french", device=device)
-# whisper_model_name = "openai/whisper-small"
-# whisper_processor = WhisperProcessor.from_pretrained(whisper_model_name)
-# whisper_model = WhisperForConditionalGeneration.from_pretrained(whisper_model_name)
-# decoder_ids = whisper_processor.get_decoder_prompt_ids(language=target_language, task="transcribe")
 
 # load text-to-speech checkpoint
 model = VitsModel.from_pretrained("facebook/mms-tts-fra")
@@ -28,21 +22,6 @@ def translate(audio):
     outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": target_language})
     return outputs["text"]
 
-# def translate(audio):
-#     if isinstance(audio, str):
-#         # Account for recorded audio
-#         audio = {
-#             "path": audio,
-#             "sampling_rate": 16_000,
-#             "array": librosa.load(audio, sr=16_000)[0]
-#         }
-#     elif audio["sampling_rate"] != 16_000:
-#         audio["array"] = librosa.resample(audio["array"], audio["sampling_rate"], 16_000)
-#     input_features = whisper_processor(audio["array"], sampling_rate=16000, return_tensors="pt").input_features
-#     predicted_ids = whisper_model.generate(input_features, forced_decoder_ids=decoder_ids)
-#     translated_text = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
-#     return translated_text
-
 
 def synthesise(text):
     inputs = tokenizer(text, return_tensors="pt")
@@ -55,7 +34,6 @@ def synthesise(text):
 
 def speech_to_speech_translation(audio):
     translated_text = translate(audio)
-    logging.info(f"Translated Text: {translated_text}")
     synthesised_speech = synthesise(translated_text)
     synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
     return 16000, synthesised_speech