Update app.py

app.py

@@ -5,14 +5,11 @@ import pytz
 from pathlib import Path
 
 def current_time():
-	current = datetime.datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y年-%m月-%d日 %H时:%M分:%S秒")
-	return current
+    current = datetime.datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y年-%m月-%d日 %H时:%M分:%S秒")
+    return current
 
 print(f"[{current_time()}] 开始部署空间...")
-"""
-print(f"[{current_time()}] 日志：安装 - 必要包")
-os.system("pip install -r ./requirements.txt")
-"""
+
 print(f"[{current_time()}] 日志：安装 - gsutil")
 os.system("pip install gsutil")
 print(f"[{current_time()}] 日志：Git - 克隆 Github 的 T5X 训练框架到当前目录")
@@ -30,7 +27,6 @@ os.system("pip install langchain")
 print(f"[{current_time()}] 日志：安装 - sentence-transformers")
 os.system("pip install sentence-transformers")
 
-# 安装 airio
 print(f"[{current_time()}] 日志：Git - 克隆 Github 的 airio 到当前目录")
 os.system("git clone --branch=main https://github.com/google/airio")
 print(f"[{current_time()}] 日志：文件 - 移动 airio 到当前目录并重命名为 airio_tmp 并删除")
@@ -38,7 +34,6 @@ os.system("mv airio airio_tmp; mv airio_tmp/* .; rm -r airio_tmp")
 print(f"[{current_time()}] 日志：Python - 使用 pip 安装 当前目录内的 Python 包")
 os.system("python3 -m pip install -e .")
 
-# 安装 mt3
 print(f"[{current_time()}] 日志：Git - 克隆 Github 的 MT3 模型到当前目录")
 os.system("git clone --branch=main https://github.com/magenta/mt3")
 print(f"[{current_time()}] 日志：文件 - 移动 mt3 到当前目录并重命名为 mt3_tmp 并删除")
@@ -46,33 +41,28 @@ os.system("mv mt3 mt3_tmp; mv mt3_tmp/* .; rm -r mt3_tmp")
 print(f"[{current_time()}] 日志：Python - 使用 pip 从 storage.googleapis.com 安装 jax[cuda11_local] nest-asyncio pyfluidsynth")
 os.system("python3 -m pip install jax[cuda11_local] nest-asyncio pyfluidsynth==1.3.0 -e . -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html")
 print(f"[{current_time()}] 日志：安装 - 更新 jaxlib")
-os.system("pip install --upgrade jaxlib==0.4.27")
+os.system("pip install --upgrade jaxlib")
 print(f"[{current_time()}] 日志：Python - 使用 pip 安装 当前目录内的 Python 包")
 os.system("python3 -m pip install -e .")
 print(f"[{current_time()}] 日志：安装 - TensorFlow CPU")
 os.system("pip install tensorflow_cpu")
 
-# 复制检查点
 print(f"[{current_time()}] 日志：gsutil - 复制 MT3 检查点到当前目录")
 os.system("gsutil -q -m cp -r gs://mt3/checkpoints .")
 
-# 复制 soundfont 文件（原始文件来自 https://sites.google.com/site/soundfonts4u）
 print(f"[{current_time()}] 日志：gsutil - 复制 SoundFont 文件到当前目录")
 os.system("gsutil -q -m cp gs://magentadata/soundfonts/SGM-v2.01-Sal-Guit-Bass-V1.3.sf2 .")
 
-#@title 导入和定义
 print(f"[{current_time()}] 日志：导入 - 必要工具")
 import functools
 import os
 import numpy as np
 import tensorflow.compat.v2 as tf
 
-import functools
 import gin
 import jax
 import librosa
 import note_seq
-
 import seqio
 import t5
 import t5x
@@ -92,230 +82,132 @@ SAMPLE_RATE = 16000
 SF2_PATH = 'SGM-v2.01-Sal-Guit-Bass-V1.3.sf2'
 
 def upload_audio(audio, sample_rate):
-	return note_seq.audio_io.wav_data_to_samples_librosa(
-		audio, sample_rate=sample_rate)
+    return note_seq.audio_io.wav_data_to_samples_librosa(
+        audio, sample_rate=sample_rate)
 
 
 print(f"[{current_time()}] 日志：开始包装模型...")
 class InferenceModel(object):
-	"""音乐转录的 T5X 模型包装器。"""
-
-	def __init__(self, checkpoint_path, model_type='mt3'):
-
-		# 模型常量。
-		if model_type == 'ismir2021':
-			num_velocity_bins = 127
-			self.encoding_spec = note_sequences.NoteEncodingSpec
-			self.inputs_length = 512
-		elif model_type == 'mt3':
-			num_velocity_bins = 1
-			self.encoding_spec = note_sequences.NoteEncodingWithTiesSpec
-			self.inputs_length = 256
-		else:
-			raise ValueError('unknown model_type: %s' % model_type)
-
-		gin_files = ['/home/user/app/mt3/gin/model.gin',
-					'/home/user/app/mt3/gin/mt3.gin']
-
-		self.batch_size = 8
-		self.outputs_length = 1024
-		self.sequence_length = {'inputs': self.inputs_length,
-								'targets': self.outputs_length}
-
-		self.partitioner = t5x.partitioning.PjitPartitioner(
-				model_parallel_submesh=None, num_partitions=1)
-
-		# 构建编解码器和词汇表。
-		print(f"[{current_time()}] 日志：构建编解码器")
-		self.spectrogram_config = spectrograms.SpectrogramConfig()
-		self.codec = vocabularies.build_codec(
-				vocab_config=vocabularies.VocabularyConfig(
-				num_velocity_bins=num_velocity_bins)
-				)
-		self.vocabulary = vocabularies.vocabulary_from_codec(self.codec)
-		self.output_features = {
-				'inputs': seqio.ContinuousFeature(dtype=tf.float32, rank=2),
-				'targets': seqio.Feature(vocabulary=self.vocabulary),
-		}
-
-		# 创建 T5X 模型。
-		print(f"[{current_time()}] 日志：创建 T5X 模型")
-		self._parse_gin(gin_files)
-		self.model = self._load_model()
-
-		# 从检查点中恢复。
-		print(f"[{current_time()}] 日志：恢复模型检查点")
-		self.restore_from_checkpoint(checkpoint_path)
-
-	@property
-	def input_shapes(self):
-		return {
-					'encoder_input_tokens': (self.batch_size, self.inputs_length),
-					'decoder_input_tokens': (self.batch_size, self.outputs_length)
-		}
-
-	def _parse_gin(self, gin_files):
-		"""解析用于训练模型的 gin 文件。"""
-		print(f"[{current_time()}] 日志：解析 gin 文件")
-		gin_bindings = [
-				'from __gin__ import dynamic_registration',
-				'from mt3 import vocabularies',
-				'[email protected]()',
-				'vocabularies.VocabularyConfig.num_velocity_bins=%NUM_VELOCITY_BINS'
-		]
-		with gin.unlock_config():
-			gin.parse_config_files_and_bindings(
-					gin_files, gin_bindings, finalize_config=False)
-
-	def _load_model(self):
-		"""在解析训练 gin 配置后加载 T5X `Model`。"""
-		print(f"[{current_time()}] 日志：加载 T5X 模型")
-		model_config = gin.get_configurable(network.T5Config)()
-		module = network.Transformer(config=model_config)
-		return models.ContinuousInputsEncoderDecoderModel(
-				module=module,
-				input_vocabulary=self.output_features['inputs'].vocabulary,
-				output_vocabulary=self.output_features['targets'].vocabulary,
-				optimizer_def=t5x.adafactor.Adafactor(decay_rate=0.8, step_offset=0),
-				input_depth=spectrograms.input_depth(self.spectrogram_config))
-
-
-	def restore_from_checkpoint(self, checkpoint_path):
-		"""从检查点中恢复训练状态，重置 self._predict_fn()。"""
-		print(f"[{current_time()}] 日志：从检查点恢复训练状态")
-		train_state_initializer = t5x.utils.TrainStateInitializer(
-			optimizer_def=self.model.optimizer_def,
-			init_fn=self.model.get_initial_variables,
-			input_shapes=self.input_shapes,
-			partitioner=self.partitioner)
-
-		restore_checkpoint_cfg = t5x.utils.RestoreCheckpointConfig(
-				path=checkpoint_path, mode='specific', dtype='float32')
-
-		train_state_axes = train_state_initializer.train_state_axes
-		self._predict_fn = self._get_predict_fn(train_state_axes)
-		self._train_state = train_state_initializer.from_checkpoint_or_scratch(
-				[restore_checkpoint_cfg], init_rng=jax.random.PRNGKey(0))
-
-	@functools.lru_cache()
-	def _get_predict_fn(self, train_state_axes):
-		"""生成一个分区的预测函数用于解码。"""
-		print(f"[{current_time()}] 日志：生成用于解码的预测函数")
-		def partial_predict_fn(params, batch, decode_rng):
-			return self.model.predict_batch_with_aux(
-					params, batch, decoder_params={'decode_rng': None})
-		return self.partitioner.partition(
-				partial_predict_fn,
-				in_axis_resources=(
-						train_state_axes.params,
-						t5x.partitioning.PartitionSpec('data',), None),
-				out_axis_resources=t5x.partitioning.PartitionSpec('data',)
-		)
-
-	def predict_tokens(self, batch, seed=0):
-		"""从预处理的数据集批次中预测 tokens。"""
-		print(f"[{current_time()}] 运行：从预处理数据集中预测音符序列")
-		prediction, _ = self._predict_fn(
-self._train_state.params, batch, jax.random.PRNGKey(seed))
-		return self.vocabulary.decode_tf(prediction).numpy()
-
-	def __call__(self, audio):
-		"""从音频样本推断出音符序列。
-
-		参数：
-			audio：16kHz 的单个音频样本的 1 维 numpy 数组。
-		返回：
-			转录音频的音符序列。
-		"""
-		print(f"[{current_time()}] 运行：从音频样本中推断音符序列")
-		ds = self.audio_to_dataset(audio)
-		ds = self.preprocess(ds)
-
-		model_ds = self.model.FEATURE_CONVERTER_CLS(pack=False)(
-				ds, task_feature_lengths=self.sequence_length)
-		model_ds = model_ds.batch(self.batch_size)
-
-		inferences = (tokens for batch in model_ds.as_numpy_iterator()
-									for tokens in self.predict_tokens(batch))
-
-		predictions = []
-		for example, tokens in zip(ds.as_numpy_iterator(), inferences):
-			predictions.append(self.postprocess(tokens, example))
-
-		result = metrics_utils.event_predictions_to_ns(
-				predictions, codec=self.codec, encoding_spec=self.encoding_spec)
-		return result['est_ns']
-
-	def audio_to_dataset(self, audio):
-		"""从输入音频创建一个包含频谱图的 TF Dataset。"""
-		print(f"[{current_time()}] 运行：从音频创建包含频谱图的 TF Dataset")
-		frames, frame_times = self._audio_to_frames(audio)
-		return tf.data.Dataset.from_tensors({
-				'inputs': frames,
-				'input_times': frame_times,
-		})
-
-	def _audio_to_frames(self, audio):
-		"""从音频计算频谱图帧。"""
-		print(f"[{current_time()}] 运行：从音频计算频谱图帧")
-		frame_size = self.spectrogram_config.hop_width
-		padding = [0, frame_size - len(audio) % frame_size]
-		audio = np.pad(audio, padding, mode='constant')
-		frames = spectrograms.split_audio(audio, self.spectrogram_config)
-		num_frames = len(audio) // frame_size
-		times = np.arange(num_frames) / self.spectrogram_config.frames_per_second
-		return frames, times
-
-	def preprocess(self, ds):
-		pp_chain = [
-				functools.partial(
-						t5.data.preprocessors.split_tokens_to_inputs_length,
-						sequence_length=self.sequence_length,
-						output_features=self.output_features,
-						feature_key='inputs',
-						additional_feature_keys=['input_times']),
-				# 在训练期间进行缓存。
-				preprocessors.add_dummy_targets,
-				functools.partial(
-						preprocessors.compute_spectrograms,
-						spectrogram_config=self.spectrogram_config)
-		]
-		for pp in pp_chain:
-			ds = pp(ds)
-		return ds
-
-	def postprocess(self, tokens, example):
-		tokens = self._trim_eos(tokens)
-		start_time = example['input_times'][0]
-		# 向下取整到最接近的符号化时间步。
-		start_time -= start_time % (1 / self.codec.steps_per_second)
-		return {
-				'est_tokens': tokens,
-				'start_time': start_time,
-				# 内部 MT3 代码期望原始输入，这里不使用。
-				'raw_inputs': []
-		}
-
-	@staticmethod
-	def _trim_eos(tokens):
-		tokens = np.array(tokens, np.int32)
-		if vocabularies.DECODED_EOS_ID in tokens:
-			tokens = tokens[:np.argmax(tokens == vocabularies.DECODED_EOS_ID)]
-		return tokens
-
-
-inference_model = InferenceModel('/home/user/app/checkpoints/mt3/', 'mt3')
-
-
-def inference(audio):
-	filename = os.path.basename(audio)  # 获取输入文件的文件名
-	print(f"[{current_time()}] 运行：输入文件: {filename}")
-	with open(audio, 'rb') as fd:
-		contents = fd.read()
-	audio = upload_audio(contents,sample_rate=16000)
-	est_ns = inference_model(audio)
-	note_seq.sequence_proto_to_midi_file(est_ns, './transcribed.mid')
-	return './transcribed.mid'
+    """音乐转录的 T5X 模型包装器。"""
+
+    def __init__(self, checkpoint_path, model_type='mt3'):
+        if model_type == 'ismir2021':
+            num_velocity_bins = 127
+            self.encoding_spec = note_sequences.NoteEncodingSpec
+            self.inputs_length = 512
+        elif model_type == 'mt3':
+            num_velocity_bins = 1
+            self.encoding_spec = note_sequences.NoteEncodingWithTiesSpec
+            self.inputs_length = 256
+        else:
+            raise ValueError('unknown model_type: %s' % model_type)
+
+        gin_files = ['/home/user/app/mt3/gin/model.gin',
+                    '/home/user/app/mt3/gin/mt3.gin']
+
+        self.batch_size = 8
+        self.outputs_length = 1024
+        self.sequence_length = {'inputs': self.inputs_length,
+                                'targets': self.outputs_length}
+
+        self.partitioner = t5x.partitioning.PjitPartitioner(
+                model_parallel_submesh=None, num_partitions=1)
+
+        print(f"[{current_time()}] 日志：构建编解码器")
+        self.spectrogram_config = spectrograms.SpectrogramConfig()
+        self.codec = vocabularies.build_codec(
+                vocab_config=vocabularies.VocabularyConfig(
+                num_velocity_bins=num_velocity_bins)
+                )
+        self.vocabulary = vocabularies.vocabulary_from_codec(self.codec)
+        self.output_features = {
+                'inputs': seqio.ContinuousFeature(dtype=tf.float32, rank=2),
+                'targets': seqio.Feature(vocabulary=self.vocabulary),
+        }
+
+        print(f"[{current_time()}] 日志：创建 T5X 模型")
+        self._parse_gin(gin_files)
+        self.model = self._load_model()
+
+        print(f"[{current_time()}] 日志：恢复模型检查点")
+        self.restore_from_checkpoint(checkpoint_path)
+
+    @property
+    def input_shapes(self):
+        return {
+                    'encoder_input_tokens': (self.batch_size, self.inputs_length),
+                    'decoder_input_tokens': (self.batch_size, self.outputs_length)
+        }
+
+    def _parse_gin(self, gin_files):
+        print(f"[{current_time()}] 日志：解析 gin 文件")
+        gin_bindings = [
+                'from __gin__ import dynamic_registration',
+                'from mt3 import vocabularies',
+                '[email protected]()',
+                'vocabularies.VocabularyConfig.num_velocity_bins=%NUM_VELOCITY_BINS'
+        ]
+        with gin.unlock_config():
+            gin.parse_config_files_and_bindings(
+                    gin_files, gin_bindings, finalize_config=False)
+
+    def _load_model(self):
+        print(f"[{current_time()}] 日志：加载 T5X 模型")
+        model_config = gin.get_configurable(network.T5Config)()
+        module = network.Transformer(config=model_config)
+        return models.ContinuousInputsEncoderDecoderModel(
+                module=module,
+                input_vocabulary=self.output_features['inputs'].vocabulary,
+                output_vocabulary=self.output_features['targets'].vocabulary,
+                optimizer_def=t5x.adafactor.Adafactor(decay_rate=0.8, step_offset=0),
+                input_depth=spectrograms.input_depth(self.spectrogram_config))
+
+
+    def restore_from_checkpoint(self, checkpoint_path):
+        print(f"[{current_time()}] 日志：从检查点恢复训练状态")
+        train_state_initializer = t5x.utils.TrainStateInitializer(
+            optimizer_def=self.model.optimizer_def,
+            init_fn=self.model.get_initial_variables,
+            input_shapes=self.input_shapes,
+            partitioner=self.partitioner)
+
+        restore_checkpoint_cfg = t5x.utils.RestoreCheckpointConfig(
+                path=checkpoint_path, mode='specific', dtype='float32')
+
+        train_state_axes = train_state_initializer.train_state_axes
+        self._predict_fn = self._get_predict_fn(train_state_axes)
+        self._train_state = train_state_initializer.from_checkpoint_or_scratch(
+                [restore_checkpoint_cfg], init_rng=jax.random.PRNGKey(0))
+
+    @functools.lru_cache()
+    def _get_predict_fn(self, train_state_axes):
+        print(f"[{current_time()}] 日志：生成用于解码的预测函数")
+        def partial_predict_fn(params, batch, decode_rng):
+            return self.model.predict_batch_with_aux(
+                    params, batch, decoder_params={'decode_rng': None})
+        return self.partitioner.partition(
+                partial_predict_fn,
+                in_axis_resources=(
+                        train_state_axes.params,
+                        t5x.partitioning.PartitionSpec('data',), None),
+                out_axis_resources=t5x.partitioning.PartitionSpec('data',)
+        )
+
+    def predict_tokens(self, batch, seed=0):
+        print(f"[{current_time()}] 运行：从预处理数据集中预测音符序列")
+        prediction, _ = self._predict_fn(
+    self._train_state.params, batch, jax.random.PRNGKey(seed))
+        return self.vocabulary.decode_tf(prediction).numpy()
+
+    def __call__(self, audio):
+        filename = os.path.basename(audio)  # 获取输入文件的文件名
+        print(f"[{current_time()}] 运行：输入文件: {filename}")
+        with open(audio, 'rb') as fd:
+            contents = fd.read()
+        audio = upload_audio(contents,sample_rate=16000)
+        est_ns = inference_model(audio)
+        note_seq.sequence_proto_to_midi_file(est_ns, './transcribed.mid')
+        return './transcribed.mid'
 
 title = "MT3"
 description = "MT3：多任务多音轨音乐转录的 Gradio 演示。要使用它，只需上传音频文件，或点击示例以查看效果。更多信息请参阅下面的链接。"
@@ -325,11 +217,11 @@ article = "<p style='text-align: center'>出错了？试试把文件转换为MP3
 examples=[['canon.flac'], ['download.wav']]
 
 gr.Interface(
-	inference,
-	gr.Audio(type="filepath", label="输入"),
-	outputs = gr.File(label="输出"),
-	title=title,
-	description=description,
-	article=article,
-	examples=examples
-	).launch()
+    inference,
+    gr.Audio(type="filepath", label="输入"),
+    outputs=gr.File(label="输出"),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples
+).launch(server_port=7861)