Update app.py

app.py

@@ -1,29 +1,130 @@
-from transformers import pipeline
-from gradio import Interface, Audio
-
-# Create a speech-to-text pipeline
-speech_to_text = pipeline("automatic-speech-recognition", model="nvidia/canary-1b")
-
-# Define a function to convert speech to text
-def convert_speech_to_text(audio):
-    # The pipeline function expects a path to an audio file, so we need to save the audio first
-    with open('temp.wav', 'wb') as f:
-        f.write(audio)
-    # Use the pipeline to convert speech to text
-    result = speech_to_text('temp.wav')
-    return result[0]['text']
-
-# Create a Gradio interface
-iface = Interface(
-    fn=convert_speech_to_text, 
-    inputs=Audio(source="microphone", type="file"), 
-    outputs="text"
+import gradio as gr
+import json
+import librosa
+import os
+import soundfile as sf
+import tempfile
+import uuid
+
+import torch
+
+from nemo.collections.asr.models import ASRModel
+from nemo.collections.asr.parts.utils.streaming_utils import FrameBatchMultiTaskAED
+from nemo.collections.asr.parts.utils.transcribe_utils import get_buffered_pred_feat_multitaskAED
+
+SAMPLE_RATE = 16000 # Hz
+MAX_AUDIO_MINUTES = 10 # wont try to transcribe if longer than this
+
+model = ASRModel.from_pretrained("nvidia/canary-1b")
+model.eval()
+
+# make sure beam size always 1 for consistency
+model.change_decoding_strategy(None)
+decoding_cfg = model.cfg.decoding
+decoding_cfg.beam.beam_size = 1
+model.change_decoding_strategy(decoding_cfg)
+
+# setup for buffered inference
+model.cfg.preprocessor.dither = 0.0
+model.cfg.preprocessor.pad_to = 0
+
+feature_stride = model.cfg.preprocessor['window_stride']
+model_stride_in_secs = feature_stride * 8 # 8 = model stride, which is 8 for FastConformer
+
+frame_asr = FrameBatchMultiTaskAED(
+	asr_model=model,
+	frame_len=40.0,
+	total_buffer=40.0,
+	batch_size=16,
 )
 
-# Launch the interface
+amp_dtype = torch.float16
+
+
+def convert_audio(audio_filepath, tmpdir, utt_id):
+	"""
+	Convert all files to monochannel 16 kHz wav files.
+	Do not convert and raise error if audio too long.
+	Returns output filename and duration.
+	"""
+
+	data, sr = librosa.load(audio_filepath, sr=None, mono=True)
+
+	duration = librosa.get_duration(y=data, sr=sr)
+
+	if duration / 60.0 > MAX_AUDIO_MINUTES:
+		raise gr.Error(
+			f"This demo can transcribe up to {MAX_AUDIO_MINUTES} minutes of audio. "
+			"If you wish, you may trim the audio using the Audio viewer in Step 1 "
+			"(click on the scissors icon to start trimming audio)."
+		)
+
+	if sr != SAMPLE_RATE:
+		data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)
+
+	out_filename = os.path.join(tmpdir, utt_id + '.wav')
+
+	# save output audio
+	sf.write(out_filename, data, SAMPLE_RATE)
+
+	return out_filename, duration
+
+def transcribe(audio_filepath):
+
+	if audio_filepath is None:
+		raise gr.Error("Please provide some input audio: either upload an audio file or use the microphone")
+	
+	utt_id = uuid.uuid4()
+	with tempfile.TemporaryDirectory() as tmpdir:
+		converted_audio_filepath, duration = convert_audio(audio_filepath, tmpdir, str(utt_id))
+        
+		# make manifest file and save
+		manifest_data = {
+			"audio_filepath": converted_audio_filepath,
+			"source_lang": "en",
+			"target_lang": "en",
+			"taskname": "asr",
+			"pnc": "no",
+			"answer": "predict",
+			"duration": str(duration),
+		}
+
+		manifest_filepath = os.path.join(tmpdir, f'{utt_id}.json')
+
+		with open(manifest_filepath, 'w') as fout:
+			line = json.dumps(manifest_data)
+			fout.write(line + '\n')
+
+		# call transcribe, passing in manifest filepath
+		if duration < 40:
+			output_text = model.transcribe(manifest_filepath)[0]
+		else: # do buffered inference
+			with torch.cuda.amp.autocast(dtype=amp_dtype): # TODO: make it work if no cuda
+				with torch.no_grad():
+					hyps = get_buffered_pred_feat_multitaskAED(
+						frame_asr,
+						model.cfg.preprocessor,
+						model_stride_in_secs,
+						model.device,
+						manifest=manifest_filepath,
+						filepaths=None,
+					)
+
+					output_text = hyps[0].text
+
+	return output_text
+
+
+iface = gr.Interface(
+    fn=transcribe, 
+    inputs=gr.Audio(sources="microphone", type="filepath"), 
+    outputs="text")
+
+iface.queue()
 iface.launch()
 
 
 
 
 
+