Update app.py

app.py

@@ -1,333 +1,345 @@
-import gradio as gr
 import os
-import datetime
-import pytz
-from pathlib import Path
+os.system("pip install gradio==3.50")
 
-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
+import gradio as gr
 
-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")
+
+from pathlib import Path
 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")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
 os.system("git clone --branch=main https://github.com/google-research/t5x")
-print(f"[{current_time()}] 日志：文件 - 移动 t5x 到当前目录并重命名为 t5x_tmp 并删除")
+
 os.system("mv t5x t5x_tmp; mv t5x_tmp/* .; rm -r t5x_tmp")
-print(f"[{current_time()}] 日志：编辑 - 替换 setup.py 内的文本“jax[tpu]”为“jax”")
+
 os.system("sed -i 's:jax\[tpu\]:jax:' setup.py")
-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：Python - 更新 Python 包管理器 pip")
+
 os.system("python3 -m pip install --upgrade pip")
-print(f"[{current_time()}] 日志：安装 - langchain")
+
 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")
-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 .")
+# install mt3
+
+
+
+
+
+
+
+
 
-# 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")
+
 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：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")
+# copy checkpoints
+
 
-# 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")
+# copy soundfont (originally from https://sites.google.com/site/soundfonts4u)
+
 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")
+#@title Imports and Definitions
+
+
+
+
+
 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
-
-from mt3 import metrics_utils
-from mt3 import models
-from mt3 import network
-from mt3 import note_sequences
-from mt3 import preprocessors
 from mt3 import spectrograms
 from mt3 import vocabularies
 
+
 import nest_asyncio
 nest_asyncio.apply()
 
-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
+  """Wrapper of T5X model for music transcription."""
+
+  def __init__(self, checkpoint_path, model_type='mt3'):
+
+    # Model Constants.
+    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)
+
+    # Build Codecs and Vocabularies.
+    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),
+    }
+
+    # Create a T5X model.
+    self._parse_gin(gin_files)
+    self.model = self._load_model()
+
+    # Restore from checkpoint.
+    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):
+    """Parse gin files used to train the model."""
+    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):
+    """Load up a T5X `Model` after parsing training gin config."""
+    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):
+    """Restore training state from checkpoint, resets self._predict_fn()."""
+    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):
+    """Generate a partitioned prediction function for decoding."""
+    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):
+    """Predict tokens from preprocessed dataset batch."""
+    prediction, _ = self._predict_fn(
+        self._train_state.params, batch, jax.random.PRNGKey(seed))
+    return self.vocabulary.decode_tf(prediction).numpy()
+
+  def __call__(self, audio):
+    """Infer note sequence from audio samples.
+    
+    Args:
+      audio: 1-d numpy array of audio samples (16kHz) for a single example.
+    Returns:
+      A note_sequence of the transcribed audio.
+    """
+    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):
+    """Create a TF Dataset of spectrograms from input audio."""
+    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):
+    """Compute spectrogram frames from audio."""
+    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']),
+        # Cache occurs here during training.
+        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]
+    # Round down to nearest symbolic token step.
+    start_time -= start_time % (1 / self.codec.steps_per_second)
+    return {
+        'est_tokens': tokens,
+        'start_time': start_time,
+        # Internal MT3 code expects raw inputs, not used here.
+        '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'
-
+  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。"
+description = "Gradio demo for MT3: Multi-Task Multitrack Music Transcription. To use it, simply upload your audio file, or click one of the examples to load them. Read more at the links below."
 
-article = "<p style='text-align: center'>出错了？试试把文件转换为MP3后再上传吧~</p><p style='text-align: center'><a href='https://arxiv.org/abs/2111.03017' target='_blank'>MT3: 多任务多音轨音乐转录</a> | <a href='https://github.com/hmjz100/mt3' target='_blank'>Github 仓库</a></p>"
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.03017' target='_blank'>MT3: Multi-Task Multitrack Music Transcription</a> | <a href='https://github.com/magenta/mt3' target='_blank'>Github Repo</a></p>"
 
 examples=[['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.inputs.Audio(type="filepath", label="Input"), 
+    [gr.outputs.File(label="Output")],
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    allow_flagging=False,
+    allow_screenshot=False,
+    enable_queue=True
+    ).launch()