Delete whisper2.py

whisper2.py

@@ -1,114 +0,0 @@
-from transformers import WhisperForConditionalGeneration, WhisperProcessor
-import torchaudio
-import torch
-import librosa
-import ffmpeg 
-
-MODEL_NAME = "openai/whisper-large-v3"
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-print("[ INFO ] Device: ", device)
-#torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-torch_dtype = torch.float32
-
-model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME, torch_dtype=torch_dtype).to(device)
-processor = WhisperProcessor.from_pretrained(MODEL_NAME)
-
-
-def convert_forced_to_tokens(forced_decoder_ids):
-    forced_decoder_tokens = []
-    for i, (idx, token) in enumerate(forced_decoder_ids):
-        if token is not None:
-            forced_decoder_tokens.append([idx, processor.tokenizer.decode(token)])
-        else:
-            forced_decoder_tokens.append([idx, token])
-    return forced_decoder_tokens
-
-
-def change_formate(input_file):
-    ffmpeg.input(input_file).output("output.wav",  **{'ar': '16000'}).run(overwrite_output=True) #loglevel='quiet'
-    return "output.wav"
-
-
-def generate(audio):
-    # audio = change_formate(audio)
-    input_audio, sample_rate = torchaudio.load(audio)
-    input_audio = torchaudio.transforms.Resample(sample_rate, 16000)(input_audio)
-    #metadata = torchaudio.info(audio)
-    #length1 = math.ceil(metadata.num_frames / metadata.sample_rate)
-    length = librosa.get_duration(path=audio)
-
-    input_speech = input_audio[0]
-
-
-    if length <= 30:
-        input_features = processor(input_speech, 
-                                    sampling_rate=16_000, 
-                                    return_tensors="pt", torch_dtype=torch_dtype).input_features.to(device)
-
-    else:
-        input_features = processor(input_speech, 
-                                return_tensors="pt", 
-                                truncation=False, 
-                                padding="longest", 
-                                return_attention_mask=True,
-                                sampling_rate=16_000).input_features.to(device)
-    forced_decoder_ids = []
-    forced_decoder_ids.append([1,50270]) #[1, '<|ca|>']
-    forced_decoder_ids.append([2,50262]) #[2, '<|es|>']
-    forced_decoder_ids.append([3,50360]) #[3, '<|transcribe|>']
-
-    forced_decoder_ids_modified = forced_decoder_ids
-    idx = processor.tokenizer.all_special_tokens.index("<|startofprev|>")
-    forced_bos_token_id = processor.tokenizer.all_special_ids[idx]
-    
-    prompt = " transcribe an audio containing code-switching between es and ca"
-    prompt_tokens = processor.tokenizer(prompt, add_special_tokens=False).input_ids
-
-    # we need to force these tokens
-    forced_decoder_ids = []
-    for idx, token in enumerate(prompt_tokens):
-        # indexing starts from 1 for forced tokens (token at position 0 is the SOS token)
-        forced_decoder_ids.append([idx + 1, token])
-        
-    # now we add the SOS token at the end
-    offset = len(forced_decoder_ids)
-    forced_decoder_ids.append([offset + 1, model.generation_config.decoder_start_token_id])
-
-    # now we need to append the rest of the prefix tokens (lang, task, timestamps)
-    offset = len(forced_decoder_ids)
-    for idx, token in forced_decoder_ids_modified:
-        forced_decoder_ids.append([idx + offset , token])
-
-    model.config.forced_decoder_ids = forced_decoder_ids
-    model.generation_config.forced_decoder_ids = forced_decoder_ids
-
-
-    if length <= 30:
-        pred_ids = model.generate(input_features, 
-                                    return_timestamps=True,
-                                    decoder_start_token_id=forced_bos_token_id,  
-                                    max_new_tokens=128)
-        #exclude prompt from output
-        forced_decoder_tokens = convert_forced_to_tokens(forced_decoder_ids)
-        output = processor.decode(pred_ids[0][len(forced_decoder_tokens) + 1:], skip_special_tokens=True)
-
-    else:
-        pred_ids = model.generate(input_features,
-                                    return_timestamps=True,
-                                    decoder_start_token_id=forced_bos_token_id, 
-                                    logprob_threshold=-1.0, 
-                                    compression_ratio_threshold=1.35,
-                                    temperature=(0.0, 0.2, 0.4),
-                                    no_speech_threshold=0.1,
-                                    )
-        output = processor.batch_decode(pred_ids, skip_special_tokens=True)
-
-
-    if length <= 30:
-        return output[1:]
-    else:
-        return output[0]
-