Revert to asr_pipeline

app.py

@@ -3,42 +3,25 @@ import librosa
 import logging
 import numpy as np
 import torch
-from datasets import load_dataset
 
-from transformers import VitsModel, VitsTokenizer
-from transformers import WhisperForConditionalGeneration, WhisperProcessor
+from transformers import VitsModel, VitsTokenizer, pipeline
 
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
-target_language = "french"
+target_language = "fr"
+
 # load speech translation checkpoint
-whisper_model_name = "openai/whisper-base"
-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")
+asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
 
-# load text-to-speech checkpoint and speaker embeddings
+# load text-to-speech checkpoint
 model = VitsModel.from_pretrained("facebook/mms-tts-fra")
 tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-fra")
 
 
-
-
 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
+    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": target_language})
+    return outputs["text"]
 
 
 def synthesise(text):
@@ -88,4 +71,4 @@ with demo:
     gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
 
 logging.getLogger().setLevel(logging.INFO)
-demo.launch()
+demo.launch()