small and pipeline

app.py

@@ -13,35 +13,35 @@ device = "cuda:0" if torch.cuda.is_available() else "cpu"
 target_language = "fr"
 
 # load speech translation checkpoint
-# asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
-whisper_model_name = "openai/whisper-medium"
-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)
+# 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")
 tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-fra")
 
 
-# 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
+    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):