Update app.py

app.py

@@ -1,119 +1,22 @@
 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,
-)
-
-amp_dtype = torch.float16
-
-
-def transcribe(audio_filepath, src_lang="en", tgt_lang="en", pnc="yes"):
-
-	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))
-
-		# map src_lang and tgt_lang from long versions to short
-		LANG_LONG_TO_LANG_SHORT = {
-			"English": "en",
-			"Spanish": "es",
-			"French": "fr",
-			"German": "de",
-		}
-		if src_lang not in LANG_LONG_TO_LANG_SHORT.keys():
-			raise ValueError(f"src_lang must be one of {LANG_LONG_TO_LANG_SHORT.keys()}")
-		else:
-			src_lang = LANG_LONG_TO_LANG_SHORT[src_lang]
-		
-		if tgt_lang not in LANG_LONG_TO_LANG_SHORT.keys():
-			raise ValueError(f"tgt_lang must be one of {LANG_LONG_TO_LANG_SHORT.keys()}")
-		else:
-			tgt_lang = LANG_LONG_TO_LANG_SHORT[tgt_lang]
-		
-
-		# infer taskname from src_lang and tgt_lang
-		if src_lang == tgt_lang:
-			taskname = "asr"
-		else:
-			taskname = "s2t_translation"
-
-		# update pnc variable to be "yes" or "no"
-		pnc = "yes" if pnc else "no"
-
-		# make manifest file and save
-		manifest_data = {
-			"audio_filepath": converted_audio_filepath,
-			"source_lang": src_lang,
-			"target_lang": tgt_lang,
-			"taskname": taskname,
-			"pnc": pnc,
-			"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"), outputs="text")
-
-iface.launch()
+from transformers import pipeline
+
+model_id = "nvidia/canary-1b"  # update with your model id
+pipe = pipeline("automatic-speech-recognition", model=model_id)
+
+def transcribe_speech(filepath):
+    output = pipe(
+        filepath,
+        max_new_tokens=256,
+        generate_kwargs={
+            "task": "transcribe",
+            "language": "english",
+        },  # update with the language you've fine-tuned on
+        chunk_length_s=30,
+        batch_size=8,
+    )
+    return output["text"]
+
+demo = gr.Interface(fn=transcribe_speech, inputs=gr.Audio(sources=["microphone"]), outputs="text")
+
+demo.launch()