dev

app.py

@@ -2,21 +2,31 @@ import gradio as gr
 import numpy as np
 import torch
 from datasets import load_dataset
-
+import librosa
 from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
 
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
 # load speech translation checkpoint
-asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
+# asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
+asr_processor = WhisperProcessor.from_pretrained("openai/whisper-base")
+asr_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base").to(device)
+asr_forced_decoder_ids = asr_processor.get_decoder_prompt_ids(language="dutch", task="transcribe")
 
 
 
 # load text-to-speech checkpoint and speaker embeddings
-processor = SpeechT5Processor.from_pretrained("sanchit-gandhi/speecht5_tts_vox_nl")
+if 0:
+    processor = SpeechT5Processor.from_pretrained("sanchit-gandhi/speecht5_tts_vox_nl")
+
+    model = SpeechT5ForTextToSpeech.from_pretrained("sanchit-gandhi/speecht5_tts_vox_nl").to(device)
+if 1:
+    from transformers import VitsModel, VitsTokenizer
+    model = VitsModel.from_pretrained("Matthijs/mms-tts-fra")
+    tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-fra")
 
-model = SpeechT5ForTextToSpeech.from_pretrained("sanchit-gandhi/speecht5_tts_vox_nl").to(device)
 vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
 
 embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
@@ -24,18 +34,40 @@ speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze
 
 
 def translate(audio):
-    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate", "language":"dutch"})
-    return outputs["text"]
+    if 0:
+        outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"language":"dutch", "task":"transcribe"})
+        return outputs["text"]
+    else:
+        
+        x, sr = librosa.load(audio)
+        input_features = asr_processor(x, sampling_rate=16000,  return_tensors="pt").input_features
+        predicted_ids = asr_model.generate(input_features, forced_decoder_ids=asr_forced_decoder_ids)
+        # decode token ids to text
+        transcription = asr_processor.batch_decode(predicted_ids, skip_special_tokens=True)
+        return transcription
+
 
 
 def synthesise(text):
-    inputs = processor(text=text, return_tensors="pt")
-    speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
-    return speech.cpu()
+    if 0:
+        inputs = processor(text=text, return_tensors="pt")
+        speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
+        return speech.cpu()
+    if 1:
+        inputs = tokenizer(text, return_tensors="pt")
+        input_ids = inputs["input_ids"]
+
+
+        with torch.no_grad():
+            outputs = model(input_ids)
+
+        speech = outputs.audio[0]
+        return speech.cpu()
 
 
 def speech_to_speech_translation(audio):
     translated_text = translate(audio)
+    print(translated_text)
     synthesised_speech = synthesise(translated_text)
     synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
     return 16000, synthesised_speech

requirements.txt

@@ -1,4 +1,5 @@
 torch
-git+https://github.com/huggingface/transformers
+#git+https://github.com/huggingface/transformers
+git+https://github.com/hollance/transformers.git@6900e8ba6532162a8613d2270ec2286c3f58f57b
 datasets
 sentencepiece