Update app.py

app.py

@@ -1,61 +1,61 @@
-import torch
-import nemo.collections.asr as nemo_asr
-import gc
-import numpy as np
-import torchaudio
-
-pretrained_model_path="./stt_fa_fastconformer_hybrid_large_finetuned.nemo"
-
-# Clear up memory
-torch.cuda.empty_cache()
-gc.collect()
-model = nemo_asr.models.EncDecHybridRNNTCTCModel.restore_from(pretrained_model_path)
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-# device = 'cpu'  # You can transcribe even longer samples on the CPU, though it will take much longer !
-model = model.to(device)
-model.freeze()
-
-def transcribe(audio):
-    # 'audio' is a tuple: (sample_rate, data)
-    sample_rate, data = audio
-
-    # Convert to mono if stereo
-    if data.ndim > 1:
-        data = data.mean(axis=1)
-
-    # Ensure the model is on the correct device
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-    # Convert audio data to the expected format
-    if isinstance(data, np.ndarray):
-        audio_tensor = torch.tensor(data, dtype=torch.float32)
-    else:
-        raise ValueError("Audio data must be a numpy array")
-
-    # Resample if sample rate is not 16000
-    target_sample_rate = 16000
-    if sample_rate != target_sample_rate:
-        resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sample_rate)
-        audio_tensor = resampler(audio_tensor)
-
-    # Trim audio if longer than 30 seconds
-    max_length = 30 * target_sample_rate  # 30 seconds
-    if audio_tensor.shape[-1] > max_length:
-        audio_tensor = audio_tensor[..., :max_length]
-
-    # Transcribe
-    with torch.no_grad():
-        transcript = model.transcribe(audio_tensor)
-
-    return transcript[0][0]  # Assuming single input
-
-import gradio as gr
-
-interface = gr.Interface(
-    fn=transcribe,
-    inputs=gr.Audio(sources=["upload", "microphone"]),  # Allows both file upload and recording
-    outputs="text",
-    live=False  # Set to True for real-time transcription
-)
-
+import torch
+import nemo.collections.asr as nemo_asr
+import gc
+import numpy as np
+import torchaudio
+
+pretrained_model_path="./stt_fa_fastconformer_hybrid_large_finetuned.nemo"
+
+# Clear up memory
+torch.cuda.empty_cache()
+gc.collect()
+model = nemo_asr.models.EncDecHybridRNNTCTCModel.restore_from(pretrained_model_path)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+# device = 'cpu'  # You can transcribe even longer samples on the CPU, though it will take much longer !
+model = model.to(device)
+model.freeze()
+
+def transcribe(audio):
+    # 'audio' is a tuple: (sample_rate, data)
+    sample_rate, data = audio
+
+    # Convert to mono if stereo
+    if data.ndim > 1:
+        data = data.mean(axis=1)
+
+    # Ensure the model is on the correct device
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+    # Convert audio data to the expected format
+    if isinstance(data, np.ndarray):
+        audio_tensor = torch.tensor(data, dtype=torch.float32)
+    else:
+        raise ValueError("Audio data must be a numpy array")
+
+    # Resample if sample rate is not 16000
+    target_sample_rate = 16000
+    if sample_rate != target_sample_rate:
+        resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sample_rate)
+        audio_tensor = resampler(audio_tensor)
+
+    # Trim audio if longer than 600 seconds
+    max_length = 600 * target_sample_rate  # 600 seconds
+    if audio_tensor.shape[-1] > max_length:
+        audio_tensor = audio_tensor[..., :max_length]
+
+    # Transcribe
+    with torch.no_grad():
+        transcript = model.transcribe(audio_tensor)
+
+    return transcript[0][0]  # Assuming single input
+
+import gradio as gr
+
+interface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(sources=["upload", "microphone"]),  # Allows both file upload and recording
+    outputs="text",
+    live=False  # Set to True for real-time transcription
+)
+
 interface.launch()