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0000000000000000000000000000000000000000..c2807f7f3cebe94f4ce652ca42b80dcc5853fe2d --- /dev/null +++ b/GS_Tempo/giantsteps-tempo-dataset/annotations/tempo/986931.LOFI.bpm @@ -0,0 +1 @@ +140 \ No newline at end of file diff --git a/GS_Tempo/sft.py b/GS_Tempo/sft.py new file mode 100644 index 0000000000000000000000000000000000000000..0c59e9c863ff64dd68b1be2da3e216bfc97730d5 --- /dev/null +++ b/GS_Tempo/sft.py @@ -0,0 +1,108 @@ +import json +import os +from pydub import AudioSegment +import tqdm +import hashlib + + +EXCERPT_LENGTH = 30 * 1000 # 30 seconds in milliseconds +MIN_LENGTH = 5 * 1000 # 5 seconds in milliseconds +PATH = "/work/fast_data_yinghao/GS_Tempo" +audio_list = os.listdir(f"{PATH}/giantsteps-tempo-dataset/audio") + + +existed_uuid_list = set() + +data_samples = [] +for audio in tqdm.tqdm(audio_list): + audio_path = os.path.join(f"{PATH}/giantsteps-tempo-dataset/audio", audio) + # f"new_audio/{os.path.basename(audio)}.wav") + output = open(f"{PATH}/giantsteps-tempo-dataset/annotations_v2/tempo/{audio[:-4]}.LOFI.bpm", "r").readline().strip() + data_sample = { + "instruction": "Please estimate the tempo of the music. Such as 60.0bpm, 143.2bpm etc.", + "input": f"<|SOA|>{audio}<|EOA|>", + "output": output, + "uuid": "", + "audioid": audio, + "split": ["train"], + "task_type": {"major": ["global_MIR"], "minor": ["tempo_esitimation"]}, + "domain": "music", + "source": "GS_Tempo", + "other": {} + } + # change uuid + uuid_string = f"{data_sample['instruction']}#{data_sample['input']}#{data_sample['output']}" + unique_id = hashlib.md5(uuid_string.encode()).hexdigest()[:16] #只取前16位 + if unique_id in existed_uuid_list: + sha1_hash = hashlib.sha1(uuid_string.encode()).hexdigest()[:16] # 为了相加的时候位数对应上 # 将 MD5 和 SHA1 结果相加,并计算新的 MD5 作为最终的 UUID + unique_id = hashlib.md5((unique_id + sha1_hash).encode()).hexdigest()[:16] + existed_uuid_list.add(unique_id) + data_sample["uuid"] = f"{unique_id}" + # data_samples.append(data_sample) + + # # change uuid + # uuid_string = f"{data_sample['instruction']}#{data_sample['input']}#{data_sample['output']}" + # unique_id = hashlib.md5(uuid_string.encode()).hexdigest()[:16] #只取前16位 + # if unique_id in existed_uuid_list: + # sha1_hash = hashlib.sha1(uuid_string.encode()).hexdigest()[:16] # 为了相加的时候位数对应上 # 将 MD5 和 SHA1 结果相加,并计算新的 MD5 作为最终的 UUID + # unique_id = hashlib.md5((unique_id + sha1_hash).encode()).hexdigest()[:16] + # existed_uuid_list.add(unique_id) + # data_sample["uuid"] = f"{unique_id}" + + if not os.path.exists(audio_path): + print(f"File not found: {audio_path}") + continue + + # Load the audio file + audio = AudioSegment.from_file(audio_path) + + # Process and split the audio into 30-second excerpts + for i in range(0, len(audio), EXCERPT_LENGTH): + end = i + EXCERPT_LENGTH + if end < len(audio): + excerpt = audio[i:end] + else: + excerpt = audio[i:] + # Discard short audio clips + if len(excerpt) < MIN_LENGTH: + break + end = len(audio) + + # # Save the excerpt to the same directory with a new name + excerpt_path = f"{PATH}/new_audio/{os.path.basename(audio_path)[:-4]}_{i//EXCERPT_LENGTH}.wav" + excerpt.export(excerpt_path, format="wav") + + # Update the sample dictionary with the new audio path + # new_sample = data_sample.copy() + # new_sample["audioid"] = excerpt_path + # new_sample["input"] = f"<|SOA|>{excerpt_path}<|EOA|>" + data_samples.append({ + "instruction": "Please estimate the tempo of the music. 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